{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T15:34:58Z","timestamp":1782833698398,"version":"3.54.5"},"reference-count":264,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T00:00:00Z","timestamp":1683504000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T00:00:00Z","timestamp":1683504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Science and Technology Special Funds Project of Guangdong Province of China","award":["STKJ2021176"],"award-info":[{"award-number":["STKJ2021176"]}]},{"name":"Science and Technology Special Funds Project of Guangdong Province of China","award":["STKJ2021019"],"award-info":[{"award-number":["STKJ2021019"]}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2021ZD0111501"],"award-info":[{"award-number":["2021ZD0111501"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176147"],"award-info":[{"award-number":["62176147"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Planning Project of Guangdong Province of China","award":["2021A0505030072"],"award-info":[{"award-number":["2021A0505030072"]}]},{"name":"Science and Technology Planning Project of Guangdong Province of China","award":["2022A1515110660"],"award-info":[{"award-number":["2022A1515110660"]}]},{"name":"STU Scientific Research Foundation for Talents","award":["NTF21001"],"award-info":[{"award-number":["NTF21001"]}]},{"name":"STU Scientific Research Foundation for Talents","award":["NTF22030"],"award-info":[{"award-number":["NTF22030"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis. Intell."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Design automation is a core technology in industrial design software and an important branch of knowledge-worker automation. For example, electronic design automation (EDA) has played an important role in both academia and industry. Design automation for intelligent robots refers to the construction of unified modular graph models for the morphologies (body), controllers (brain), and vision systems (eye) of intelligent robots under digital twin architectures, which effectively supports the automation of the morphology, controller, and vision system design processes of intelligent robots by taking advantage of the powerful capabilities of genetic programming, evolutionary computation, deep learning, reinforcement learning, and causal reasoning in model representation, optimization, perception, decision making, and reasoning. Compared with traditional design methods, MOdular DEsigN Automation (MODENA) methods can significantly improve the design efficiency and performance of robots, effectively avoiding the repetitive trial-and-error processes of traditional design methods, and promoting automatic discovery of innovative designs. Thus, it is of considerable research significance to study MODENA methods for intelligent robots. To this end, this paper provides a systematic and comprehensive overview of applying MODENA in intelligent robots, analyzes the current problems and challenges in the field, and provides an outlook for future research. First, the design automation for the robot morphologies and controllers is reviewed, individually, with automated design of control strategies for swarm robots also discussed, which has emerged as a prominent research focus recently. Next, the integrated design automation of both the morphologies and controllers for robotic systems is presented. Then, the design automation of the vision systems of intelligent robots is summarized when vision systems have become one of the most important modules for intelligent robotic systems. Then, the future research trends of integrated \u201cBody-Brain-Eye\u201d design automation for intelligent robots are discussed. Finally, the common key technologies, research challenges and opportunities in MODENA for intelligent robots are summarized.<\/jats:p>","DOI":"10.1007\/s44267-023-00006-x","type":"journal-article","created":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T02:01:30Z","timestamp":1683511290000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Modular design automation of the morphologies, controllers, and vision systems for intelligent robots: a survey"],"prefix":"10.1007","volume":"1","author":[{"given":"Wenji","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaojun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruitao","family":"Mai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengxiang","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinchang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yutao","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"JiaFan","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Xin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhifeng","family":"Hao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4232-8229","authenticated-orcid":false,"given":"Zhun","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,8]]},"reference":[{"issue":"70","key":"6_CR1","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abq2296","volume":"7","author":"M. F. Reynolds","year":"2022","unstructured":"Reynolds, M. F., Cortese, A. J., Liu, Q., Zheng, Z., Wang, W., Norris, S. L., et al. (2022). Microscopic robots with onboard digital control. Science Robotics, 7(70), eabq2296.","journal-title":"Science Robotics"},{"issue":"6446","key":"6_CR2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aat8414","volume":"364","author":"A. Billard","year":"2019","unstructured":"Billard, A., & Kragic, D. (2019). Trends and challenges in robot manipulation. Science, 364(6446), eaat8414.","journal-title":"Science"},{"issue":"66","key":"6_CR3","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abm6074","volume":"7","author":"S. Macenski","year":"2022","unstructured":"Macenski, S., Foote, T., Gerkey, B., Lalancette, C., & Woodall, W. (2022). Robot operating system 2: design, architecture, and uses in the wild. Science Robotics, 7(66), eabm6074.","journal-title":"Science Robotics"},{"key":"6_CR4","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.robot.2018.12.011","volume":"113","author":"M. Honarpardaz","year":"2019","unstructured":"Honarpardaz, M., \u00d6lvander, J., & Tarkian, M. (2019). Fast finger design automation for industrial robots. Robotics and Autonomous Systems, 113, 120\u2013131.","journal-title":"Robotics and Autonomous Systems"},{"key":"6_CR5","first-page":"147","volume-title":"Biomimetics\u2014biologically inspired technologies","author":"H. Lipson","year":"2005","unstructured":"Lipson, H. (2005). Evolutionary robotics and open-ended design automation. In Y. Bar-Cohen (Ed.), Biomimetics\u2014biologically inspired technologies (pp.\u00a0147\u2013174). Boca Raton: CRC Press."},{"issue":"2","key":"6_CR6","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1007\/s10845-021-01826-8","volume":"34","author":"T. Hoebert","year":"2021","unstructured":"Hoebert, T., Lepuschitz, W., Vincze, M., & Merdan, M. (2021). Knowledge-driven framework for industrial robotic systems. Journal of Intelligent Manufacturing, 34(2), 771\u2013788.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"2","key":"6_CR7","doi-asserted-by":"publisher","first-page":"173","DOI":"10.3233\/ICA-180569","volume":"25","author":"F. Ramos","year":"2018","unstructured":"Ramos, F., V\u00e1zquez, A. S., Fern\u00e1ndez, R., & Olivares-Alarcos, A. (2018). Ontology based design, control and programming of modular robots. Integrated Computer-Aided Engineering, 25(2), 173\u2013192.","journal-title":"Integrated Computer-Aided Engineering"},{"issue":"2","key":"6_CR8","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.rcim.2010.07.013","volume":"27","author":"M. Short","year":"2011","unstructured":"Short, M., & Burn, K. (2011). A generic controller architecture for intelligent robotic systems. Robotics and Computer-Integrated Manufacturing, 27(2), 292\u2013305.","journal-title":"Robotics and Computer-Integrated Manufacturing"},{"key":"6_CR9","unstructured":"Armangu\u00e9 Quintana, X. (2003). Modelling stereoscopic vision systems for robotic applications. PhD thesis, Universitat de Girona."},{"key":"6_CR10","first-page":"1054","volume-title":"Proceedings of SAI intelligent systems conference","author":"A. Diveev","year":"2019","unstructured":"Diveev, A., & Sofronova, E. (2019). Automation of synthesized optimal control problem solution for mobile robot by genetic programming. In Proceedings of SAI intelligent systems conference (pp.\u00a01054\u20131072). Berlin: Springer."},{"issue":"3","key":"6_CR11","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1007\/s10846-018-0902-9","volume":"95","author":"R. J. Alattas","year":"2019","unstructured":"Alattas, R. J., Patel, S., & Sobh, T. M. (2019). Evolutionary modular robotics: survey and analysis. Journal of Intelligent & Robotic Systems, 95(3), 815\u2013828.","journal-title":"Journal of Intelligent & Robotic Systems"},{"issue":"16","key":"6_CR12","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1080\/01691864.2017.1365009","volume":"31","author":"H. A. Pierson","year":"2017","unstructured":"Pierson, H. A., & Gashler, M. S. (2017). Deep learning in robotics: a review of recent research. Advanced Robotics, 31(16), 821\u2013835.","journal-title":"Advanced Robotics"},{"issue":"2","key":"6_CR13","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1007\/s10462-021-09997-9","volume":"55","author":"B. Singh","year":"2022","unstructured":"Singh, B., Kumar, R., & Singh, V. P. (2022). Reinforcement learning in robotic applications: a comprehensive survey. Artificial Intelligence Review, 55(2), 945\u2013990.","journal-title":"Artificial Intelligence Review"},{"issue":"1","key":"6_CR14","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1515\/pjbr-2021-0017","volume":"12","author":"T. Hellstr\u00f6m","year":"2021","unstructured":"Hellstr\u00f6m, T. (2021). The relevance of causation in robotics: a review, categorization, and analysis. Paladyn, Journal of Behavioral Robotics, 12(1), 238\u2013255.","journal-title":"Paladyn, Journal of Behavioral Robotics"},{"issue":"6799","key":"6_CR15","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1038\/35023115","volume":"406","author":"H. Lipson","year":"2000","unstructured":"Lipson, H., & Pollack, J. B. (2000). Automatic design and manufacture of robotic lifeforms. Nature, 406(6799), 974\u2013978.","journal-title":"Nature"},{"issue":"26","key":"6_CR16","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.aau9354","volume":"4","author":"R. Kwiatkowski","year":"2019","unstructured":"Kwiatkowski, R., & Lipson, H. (2019). Task-agnostic self-modeling machines. Science Robotics, 4(26), eaau9354.","journal-title":"Science Robotics"},{"issue":"5923","key":"6_CR17","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1126\/science.1165893","volume":"324","author":"M. Schmidt","year":"2009","unstructured":"Schmidt, M., & Lipson, H. (2009). Distilling free-form natural laws from experimental data. Science, 324(5923), 81\u201385.","journal-title":"Science"},{"issue":"7039","key":"6_CR18","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1038\/435163a","volume":"435","author":"V. Zykov","year":"2005","unstructured":"Zykov, V., Mytilinaios, E., Adams, B., & Lipson, H. (2005). Self-reproducing machines. Nature, 435(7039), 163\u2013164.","journal-title":"Nature"},{"issue":"2","key":"6_CR19","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1080\/03052150410001647957","volume":"36","author":"Z. Fan","year":"2004","unstructured":"Fan, Z., Seo, K., Hu, J., Goodman, E. D., & Rosenberg, R. C. (2004). A novel evolutionary engineering design approach for mixed-domain systems. Engineering Optimization, 36(2), 127\u2013147.","journal-title":"Engineering Optimization"},{"issue":"5","key":"6_CR20","doi-asserted-by":"publisher","DOI":"10.3390\/mi12050506","volume":"12","author":"P. Xu","year":"2021","unstructured":"Xu, P., Wei, Z., Guo, Z., Jia, L., Han, G., Si, C., Ning, J., & Yang, F. (2021). A\u00a0real-time circuit phase delay correction system for MEMS vibratory gyroscopes. Micromachines, 12(5), Article No. 506.","journal-title":"Micromachines"},{"issue":"6","key":"6_CR21","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1109\/MNANO.2021.3113218","volume":"15","author":"G. Krylov","year":"2021","unstructured":"Krylov, G., Kawa, J., & Friedman, E. G. (2021). Design automation of superconductive digital circuits: a review. IEEE Nanotechnology Magazine, 15(6), 54\u201367.","journal-title":"IEEE Nanotechnology Magazine"},{"issue":"15","key":"6_CR22","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.aaz1708","volume":"6","author":"A. E. Gongora","year":"2020","unstructured":"Gongora, A. E., Xu, B., Perry, W., Okoye, C., Riley, P., Reyes, K. G., Morgan, E. F., & Brown, K. A. (2020). A Bayesian experimental autonomous researcher for mechanical design. Science Advances, 6(15), eaaz1708.","journal-title":"Science Advances"},{"issue":"4","key":"6_CR23","volume":"10","author":"A. A. Sneineh","year":"2019","unstructured":"Sneineh, A. A., & Salah, W. A. (2019). Design and implementation of an automatically aligned solar tracking system. International Journal of Power Electronics and Drive Systems, 10(4), 2055.","journal-title":"International Journal of Power Electronics and Drive Systems"},{"issue":"2","key":"6_CR24","first-page":"172","volume":"35","author":"J. Wang","year":"2005","unstructured":"Wang, J., Fan, Z., Terpenny, J. P., & Goodman, E. D. (2005). Knowledge interaction with genetic programming in mechatronic systems design using bond graphs. IEEE Transactions on Systems, Man and Cybernetics. Part C, Applications and Reviews, 35(2), 172\u2013182.","journal-title":"IEEE Transactions on Systems, Man and Cybernetics. Part C, Applications and Reviews"},{"issue":"1","key":"6_CR25","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/TMECH.2007.915058","volume":"13","author":"S. Behbahani","year":"2008","unstructured":"Behbahani, S., & de Silva, C. W. (2008). System-based and concurrent design of a smart mechatronic system using the concept of mechatronic design quotient (MDQ). IEEE\/ASME Transactions on Mechatronics, 13(1), 14\u201321.","journal-title":"IEEE\/ASME Transactions on Mechatronics"},{"issue":"3","key":"6_CR26","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1109\/TEVC.2011.2159724","volume":"16","author":"J.-F. Dupuis","year":"2012","unstructured":"Dupuis, J.-F., Fan, Z., & Goodman, E. D. (2012). Evolutionary design of both topologies and parameters of a hybrid dynamical system. IEEE Transactions on Evolutionary Computation, 16(3), 391\u2013405. https:\/\/doi.org\/10.1109\/TEVC.2011.2159724.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"20","key":"6_CR27","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.aat0430","volume":"3","author":"L. Garattoni","year":"2018","unstructured":"Garattoni, L., & Birattari, M. (2018). Autonomous task sequencing in a robot swarm. Science Robotics, 3(20), eaat0430.","journal-title":"Science Robotics"},{"key":"6_CR28","volume-title":"Mechatronic design automation: an emerging research and recent advances","author":"Z. Fan","year":"2010","unstructured":"Fan, Z. (2010). Mechatronic design automation: an emerging research and recent advances. New York: Nova Science Publishers."},{"issue":"4","key":"6_CR29","doi-asserted-by":"publisher","first-page":"295","DOI":"10.5772\/5636","volume":"1","author":"Z. Fan","year":"2004","unstructured":"Fan, Z., Wang, J., & Goodman, E. (2004). Exploring open-ended design space of mechatronic systems. International Journal of Advanced Robotic Systems, 1(4), 295\u2013302.","journal-title":"International Journal of Advanced Robotic Systems"},{"issue":"10","key":"6_CR30","doi-asserted-by":"publisher","first-page":"2017","DOI":"10.1162\/jocn_a_01544","volume":"33","author":"G. W. Lindsay","year":"2021","unstructured":"Lindsay, G. W. (2021). Convolutional neural networks as a model of the visual system: past, present, and future. Journal of Cognitive Neuroscience, 33(10), 2017\u20132031.","journal-title":"Journal of Cognitive Neuroscience"},{"key":"6_CR31","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1109\/TASLP.2021.3057230","volume":"29","author":"Y. Qian","year":"2021","unstructured":"Qian, Y., Chen, Z., & Wang, S. (2021). Audio-visual deep neural network for robust person verification. IEEE\/ACM Transactions on Audio, Speech and Language Processing, 29, 1079\u20131092.","journal-title":"IEEE\/ACM Transactions on Audio, Speech and Language Processing"},{"key":"6_CR32","unstructured":"Cai, Y., Li, H., Fan, Z., Hong, J., Xu, P., Cheng, H., Zhu, X., Hu, B., & Hao, Z. (2022). VGSwarm: a vision-based gene regulation network for UAVs swarm behavior emergence. arXiv preprint arXiv:2206.08669."},{"key":"6_CR33","unstructured":"Fan, Z., Wang, Z., Zhu, X., Hu, B., Zou, A., & Bao, D. (2019). An automatic design framework of swarm pattern formation based on multi-objective genetic programming. arXiv preprint arXiv:1910.14627."},{"key":"6_CR34","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1016\/j.asoc.2019.01.023","volume":"77","author":"J. Li","year":"2019","unstructured":"Li, J., & Tan, Y. (2019). A probabilistic finite state machine based strategy for multi-target search using swarm robotics. Applied Soft Computing, 77, 467\u2013483.","journal-title":"Applied Soft Computing"},{"issue":"2","key":"6_CR35","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1109\/TMECH.2019.2903140","volume":"24","author":"G. Xu","year":"2019","unstructured":"Xu, G., Ding, H., & Feng, Z. (2019). Optimal design of hydraulic excavator shovel attachment based on multiobjective evolutionary algorithm. IEEE\/ASME Transactions on Mechatronics, 24(2), 808\u2013819.","journal-title":"IEEE\/ASME Transactions on Mechatronics"},{"issue":"7","key":"6_CR36","doi-asserted-by":"publisher","DOI":"10.3390\/app10072223","volume":"10","author":"J. C. Hsiao","year":"2020","unstructured":"Hsiao, J. C., Shivam, K., Chou, C. L., & Kam, T. Y. (2020). Shape design optimization of a robot arm using a surrogate-based evolutionary approach. Applied Sciences, 10(7), 2223.","journal-title":"Applied Sciences"},{"key":"6_CR37","doi-asserted-by":"publisher","first-page":"1843","DOI":"10.1145\/2001576.2001823","volume-title":"Proceedings of the 13th annual conference on genetic and evolutionary computation","author":"R. Datta","year":"2011","unstructured":"Datta, R., & Deb, K. (2011). Multi-objective design and analysis of robot gripper configurations using an evolutionary-classical approach. In Proceedings of the 13th annual conference on genetic and evolutionary computation (pp.\u00a01843\u20131850). New York: ACM."},{"issue":"1","key":"6_CR38","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/TSMC.2015.2437847","volume":"46","author":"R. Datta","year":"2015","unstructured":"Datta, R., Pradhan, S., & Bhattacharya, B. (2015). Analysis and design optimization of a robotic gripper using multiobjective genetic algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(1), 16\u201326.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"6_CR39","doi-asserted-by":"publisher","first-page":"4605","DOI":"10.1109\/IROS.2014.6943215","volume-title":"2014 IEEE\/RSJ international conference on intelligent robots and systems","author":"S. Rezazadeh","year":"2014","unstructured":"Rezazadeh, S., & Hurst, J. W. (2014). On the optimal selection of motors and transmissions for electromechanical and robotic systems. In 2014 IEEE\/RSJ international conference on intelligent robots and systems (pp.\u00a04605\u20134611). Los Alamitos: IEEE."},{"issue":"4","key":"6_CR40","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1109\/TMECH.2002.806220","volume":"7","author":"S. Murata","year":"2002","unstructured":"Murata, S., Yoshida, E., Kamimura, A., Kurokawa, H., Tomita, K., & Kokaji, S. (2002). M-TRAN: self-reconfigurable modular robotic system. IEEE\/ASME Transactions on Mechatronics, 7(4), 431\u2013441.","journal-title":"IEEE\/ASME Transactions on Mechatronics"},{"issue":"5","key":"6_CR41","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1109\/TRA.2002.804502","volume":"18","author":"W.-M. Shen","year":"2002","unstructured":"Shen, W.-M., Salemi, B., & Will, P. (2002). Hormone-inspired adaptive communication and distributed control for CONRO self-reconfigurable robots. IEEE Transactions on Robotics and Automation, 18(5), 700\u2013712.","journal-title":"IEEE Transactions on Robotics and Automation"},{"key":"6_CR42","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/ICMA.2007.4303511","volume-title":"2007 international conference on mechatronics and automation","author":"D. Brandt","year":"2007","unstructured":"Brandt, D., Christensen, D. J., & Lund, H. H. (2007). ATRON robots: versatility from self-reconfigurable modules. In 2007 international conference on mechatronics and automation (pp.\u00a026\u201332). Los Alamitos: IEEE."},{"issue":"12","key":"6_CR43","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1016\/j.ifacol.2016.07.688","volume":"49","author":"C. West","year":"2016","unstructured":"West, C., Montazeri, A., Monk, S. D., & Taylor, C. J. (2016). A genetic algorithm approach for parameter optimization of a 7DOF robotic manipulator. IFAC-PapersOnLine, 49(12), 1261\u20131266.","journal-title":"IFAC-PapersOnLine"},{"key":"6_CR44","first-page":"199","volume-title":"2016 international conference on industrial informatics-computing technology, intelligent technology, industrial information integration (ICIICII)","author":"Y. Xiao","year":"2016","unstructured":"Xiao, Y., Fan, Z., Li, W., Chen, S., Zhao, L., & Xie, H. (2016). A manipulator design optimization based on constrained multi-objective evolutionary algorithms. In 2016 international conference on industrial informatics-computing technology, intelligent technology, industrial information integration (ICIICII) (pp.\u00a0199\u2013205). Los Alamitos: IEEE."},{"key":"6_CR45","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.rcim.2016.12.012","volume":"46","author":"A. Hassan","year":"2017","unstructured":"Hassan, A., & Abomoharam, M. (2017). Modeling and design optimization of a robot gripper mechanism. Robotics and Computer-Integrated Manufacturing, 46, 94\u2013103.","journal-title":"Robotics and Computer-Integrated Manufacturing"},{"key":"6_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2019.06.011","volume":"50","author":"Z. Fan","year":"2019","unstructured":"Fan, Z., You, Y., Cai, X., Zheng, H., Zhu, G., Li, W., Garg, A., Deb, K., & Goodman, E. (2019). Analysis and multi-objective optimization of a kind of teaching manipulator. Swarm and Evolutionary Computation, 50, 100554.","journal-title":"Swarm and Evolutionary Computation"},{"key":"6_CR47","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1016\/j.swevo.2018.08.017","volume":"44","author":"Z. Fan","year":"2019","unstructured":"Fan, Z., Li, W., Cai, X., Li, H., Wei, C., Zhang, Q., Deb, K., & Goodman, E. (2019). Push and pull search for solving constrained multi-objective optimization problems. Swarm and Evolutionary Computation, 44, 665\u2013679.","journal-title":"Swarm and Evolutionary Computation"},{"key":"6_CR48","doi-asserted-by":"publisher","first-page":"7216","DOI":"10.1109\/IROS51168.2021.9636305","volume-title":"2021 IEEE\/RSJ international conference on intelligent robots and systems (IROS)","author":"Z. Zhang","year":"2021","unstructured":"Zhang, Z., Zheng, Y., Hu, Z., Liu, L., Zhao, X., Li, X., & Pan, J. (2021). A computational framework for robot hand design via reinforcement learning. In 2021 IEEE\/RSJ international conference on intelligent robots and systems (IROS) (pp.\u00a07216\u20137222). Los Alamitos: IEEE."},{"issue":"4","key":"6_CR49","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1109\/TRA.2003.814502","volume":"19","author":"G. S. Hornby","year":"2003","unstructured":"Hornby, G. S., Lipson, H., & Pollack, J. B. (2003). Generative representations for the automated design of modular physical robots. IEEE Transactions on Robotics and Automation, 19(4), 703\u2013719.","journal-title":"IEEE Transactions on Robotics and Automation"},{"key":"6_CR50","first-page":"50","volume-title":"International work-conference on the interplay between natural and artificial computation","author":"A. Fa\u00ed\u00f1a","year":"2011","unstructured":"Fa\u00ed\u00f1a, A., Bellas, F., Souto, D., & Duro, R. J. (2011). Towards an evolutionary design of modular robots for industry. In International work-conference on the interplay between natural and artificial computation (pp.\u00a050\u201359). Berlin: Springer."},{"issue":"10","key":"6_CR51","doi-asserted-by":"publisher","first-page":"2408","DOI":"10.1016\/j.engappai.2013.09.009","volume":"26","author":"A. Fa\u00ed\u00f1a","year":"2013","unstructured":"Fa\u00ed\u00f1a, A., Bellas, F., L\u00f3pez-Pe\u00f1a, F., & Duro, R. J. (2013). EDHMoR: evolutionary designer of heterogeneous modular robots. Engineering Applications of Artificial Intelligence, 26(10), 2408\u20132423.","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"6_CR52","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1007\/978-3-319-55849-3_56","volume-title":"European conference on the applications of evolutionary computation","author":"F. Veenstra","year":"2017","unstructured":"Veenstra, F., Faina, A., Risi, S., & Stoy, K. (2017). Evolution and morphogenesis of simulated modular robots: a comparison between a direct and generative encoding. In European conference on the applications of evolutionary computation (pp.\u00a0870\u2013885). Berlin: Springer."},{"issue":"2","key":"6_CR53","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1162\/EVCO_a_00172","volume":"24","author":"F. Silva","year":"2016","unstructured":"Silva, F., Duarte, M., Correia, L., Oliveira, S. M., & Christensen, A. L. (2016). Open issues in evolutionary robotics. Evolutionary Computation, 24(2), 205\u2013236.","journal-title":"Evolutionary Computation"},{"issue":"25","key":"6_CR54","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.abn8932","volume":"8","author":"Y. Dong","year":"2022","unstructured":"Dong, Y., Wang, L., Xia, N., Yang, Z., Zhang, C., Pan, C., Jin, D., Zhang, J., Majidi, C., & Zhang, L. (2022). Untethered small-scale magnetic soft robot with programmable magnetization and integrated multifunctional modules. Science Advances, 8(25), eabn8932.","journal-title":"Science Advances"},{"key":"6_CR55","doi-asserted-by":"publisher","first-page":"3728","DOI":"10.1109\/IROS.2006.281754","volume-title":"2006 IEEE\/RSJ international conference on intelligent robots and systems","author":"J. Kelly","year":"2006","unstructured":"Kelly, J., & Zhang, H. (2006). Combinatorial optimization of sensing for rule-based planar distributed assembly. In 2006 IEEE\/RSJ international conference on intelligent robots and systems (pp.\u00a03728\u20133734). Los Alamitos: IEEE."},{"key":"6_CR56","unstructured":"Werfel, J. (2006). Anthills built to order: automating construction with artificial swarms. PhD thesis, Harvard University."},{"key":"6_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.mechmachtheory.2020.103931","volume":"152","author":"X. Kang","year":"2020","unstructured":"Kang, X., Feng, H., Dai, J. S., & Yu, H. (2020). High-order based revelation of bifurcation of novel Schatz-inspired metamorphic mechanisms using screw theory. Mechanism and Machine Theory, 152, 103931.","journal-title":"Mechanism and Machine Theory"},{"issue":"3","key":"6_CR58","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1115\/1.2829470","volume":"121","author":"J. S. Dai","year":"1999","unstructured":"Dai, J. S., & Rees Jones, J. (1999). Mobility in metamorphic mechanisms of foldable\/erectable kinds. Journal of Mechanical Design, 121(3), 375\u2013382.","journal-title":"Journal of Mechanical Design"},{"issue":"5","key":"6_CR59","doi-asserted-by":"publisher","DOI":"10.1115\/1.4044004","volume":"11","author":"X. Chai","year":"2019","unstructured":"Chai, X., & Dai, J. S. (2019). Three novel symmetric Waldron\u2013Bricard metamorphic and reconfigurable mechanisms and their isomerization. Journal of Mechanisms and Robotics, 11(5), 051011.","journal-title":"Journal of Mechanisms and Robotics"},{"issue":"7","key":"6_CR60","doi-asserted-by":"publisher","DOI":"10.1115\/1.2900719","volume":"130","author":"L. Zhang","year":"2008","unstructured":"Zhang, L., Wang, D., & Dai, J. S. (2008). Biological modeling and evolution based synthesis of metamorphic mechanisms. Journal of Mechanical Design, 130(7), 072303.","journal-title":"Journal of Mechanical Design"},{"issue":"1","key":"6_CR61","first-page":"318","volume":"234","author":"C. Sun","year":"2020","unstructured":"Sun, C., Chen, L., Liu, J., Dai, J. S., & Kang, R. (2020). A hybrid continuum robot based on pneumatic muscles with embedded elastic rods. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 234(1), 318\u2013328.","journal-title":"Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science"},{"issue":"6","key":"6_CR62","doi-asserted-by":"publisher","DOI":"10.1115\/1.4046837","volume":"12","author":"L. Meng","year":"2020","unstructured":"Meng, L., Kang, R., Gan, D., Chen, G., Chen, L., Branson, D. T., & Dai, J. S. (2020). A mechanically intelligent crawling robot driven by shape memory alloy and compliant bistable mechanism. Journal of Mechanisms and Robotics, 12(6), 061005.","journal-title":"Journal of Mechanisms and Robotics"},{"issue":"6","key":"6_CR63","doi-asserted-by":"publisher","DOI":"10.1115\/1.4054408","volume":"14","author":"Z. Tang","year":"2022","unstructured":"Tang, Z., Wang, K., Spyrakos-Papastavridis, E., & Dai, J. S. (2022). Origaker: a novel multi-mimicry quadruped robot based on a metamorphic mechanism. Journal of Mechanisms and Robotics, 14(6), 060907.","journal-title":"Journal of Mechanisms and Robotics"},{"key":"6_CR64","doi-asserted-by":"publisher","DOI":"10.1016\/j.mechmachtheory.2021.104245","volume":"161","author":"R. Wang","year":"2021","unstructured":"Wang, R., Song, Y., & Dai, J. S. (2021). Reconfigurability of the origami-inspired integrated 8R kinematotropic metamorphic mechanism and its evolved 6R and 4R mechanisms. Mechanism and Machine Theory, 161, 104245.","journal-title":"Mechanism and Machine Theory"},{"key":"6_CR65","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1007\/978-3-030-39831-6_30","volume-title":"Evolution in action: past, present and future","author":"Z. Fan","year":"2020","unstructured":"Fan, Z., Zhu, G., & Li, W. (2020). Mechatronic design automation: a short review. In W. Banzhaf, B.\u00a0H.\u00a0C. Cheng, K. Deb, et al. (Eds.), Evolution in action: past, present and future (pp.\u00a0453\u2013466). Berlin: Springer."},{"key":"6_CR66","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1109\/RoboSoft48309.2020.9116010","volume-title":"2020 3rd IEEE international conference on soft robotics (RoboSoft)","author":"B. Caasenbrood","year":"2020","unstructured":"Caasenbrood, B., Pogromsky, A., & Nijmeijer, H. (2020). A computational design framework for pressure-driven soft robots through nonlinear topology optimization. In 2020 3rd IEEE international conference on soft robotics (RoboSoft) (pp.\u00a0633\u2013638). Los Alamitos: IEEE."},{"key":"6_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmps.2019.103773","volume":"135","author":"Z.-L. Zhao","year":"2020","unstructured":"Zhao, Z.-L., Zhou, S., Feng, X.-Q., & Xie, Y. M. (2020). Morphological optimization of scorpion telson. Journal of the Mechanics and Physics of Solids, 135, 103773.","journal-title":"Journal of the Mechanics and Physics of Solids"},{"key":"6_CR68","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/978-1-4020-4941-5_33","volume-title":"Advances in robot kinematics, mechanisms and motion","author":"E. Ottaviano","year":"2006","unstructured":"Ottaviano, E., Husty, M., & Ceccarelli, M. (2006). Level-set method for workspace analysis of serial manipulators. In J. Lenarcic & B. Roth (Eds.), Advances in robot kinematics, mechanisms and motion (pp.\u00a0307\u2013314). Berlin: Springer."},{"key":"6_CR69","doi-asserted-by":"publisher","first-page":"2286","DOI":"10.1109\/ROBIO.2014.7090678","volume-title":"2014 IEEE international conference on robotics and biomimetics (ROBIO 2014)","author":"D. Ye","year":"2014","unstructured":"Ye, D., Sun, S., Chen, J., & Luo, M. (2014). The lightweight design of the humanoid robot frameworks based on evolutionary structural optimization. In 2014 IEEE international conference on robotics and biomimetics (ROBIO 2014) (pp.\u00a02286\u20132291). Los Alamitos: IEEE."},{"issue":"1","key":"6_CR70","doi-asserted-by":"publisher","DOI":"10.1115\/1.4041319","volume":"86","author":"X. Lei","year":"2019","unstructured":"Lei, X., Liu, C., Du, Z., Zhang, W., & Guo, X. (2019). Machine learning-driven real-time topology optimization under moving morphable component-based framework. Journal of Applied Mechanics, 86(1), 011004.","journal-title":"Journal of Applied Mechanics"},{"issue":"3","key":"6_CR71","doi-asserted-by":"publisher","first-page":"1669","DOI":"10.1007\/s00158-021-02858-7","volume":"64","author":"J. Gao","year":"2021","unstructured":"Gao, J., Wang, L., Luo, Z., & Gao, L. (2021). IgaTop: an implementation of topology optimization for structures using IGA in Matlab. Structural and Multidisciplinary Optimization, 64(3), 1669\u20131700.","journal-title":"Structural and Multidisciplinary Optimization"},{"issue":"6","key":"6_CR72","first-page":"24","volume":"33","author":"J. Gao","year":"2020","unstructured":"Gao, J., Xiao, M., Zhang, Y., & Gao, L. (2020). A comprehensive review of isogeometric topology optimization: methods, applications and prospects. Chinese Journal of Mechanical Engineering, 33(6), 24\u201337.","journal-title":"Chinese Journal of Mechanical Engineering"},{"issue":"10","key":"6_CR73","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1002\/nme.6081","volume":"119","author":"J. Gao","year":"2019","unstructured":"Gao, J., Gao, L., Luo, Z., & Li, P. (2019). Isogeometric topology optimization for continuum structures using density distribution function. International Journal for Numerical Methods in Engineering, 119(10), 991\u20131017.","journal-title":"International Journal for Numerical Methods in Engineering"},{"issue":"3","key":"6_CR74","first-page":"94","volume":"22","author":"Y. Wang","year":"2022","unstructured":"Wang, Y., Xiao, M., Xia, Z., Li, P., & Gao, L. (2022). From computer-aided design (CAD) toward human-aided design (HAD): an isogeometric topology optimization approach. Engineering, 22(3), 94\u2013105.","journal-title":"Engineering"},{"issue":"2","key":"6_CR75","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s11465-022-0683-5","volume":"17","author":"J. Gao","year":"2022","unstructured":"Gao, J., Xiao, M., Yan, Z., Gao, L., & Li, H. (2022). Robust isogeometric topology optimization for piezoelectric actuators with uniform manufacturability. Frontiers of Mechanical Engineering, 17(2), 205\u2013224.","journal-title":"Frontiers of Mechanical Engineering"},{"key":"6_CR76","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.cma.2019.04.021","volume":"352","author":"J. Gao","year":"2019","unstructured":"Gao, J., Xue, H., Gao, L., & Luo, Z. (2019). Topology optimization for auxetic metamaterials based on isogeometric analysis. Computer Methods in Applied Mechanics and Engineering, 352, 211\u2013236.","journal-title":"Computer Methods in Applied Mechanics and Engineering"},{"key":"6_CR77","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmecsci.2019.105103","volume":"166","author":"J. Xu","year":"2020","unstructured":"Xu, J., Gao, L., Xiao, M., Gao, J., & Li, H. (2020). Isogeometric topology optimization for rational design of ultra-lightweight architected materials. International Journal of Mechanical Sciences, 166, 105103.","journal-title":"International Journal of Mechanical Sciences"},{"issue":"49\u201352","key":"6_CR78","doi-asserted-by":"publisher","first-page":"3270","DOI":"10.1016\/j.cma.2010.06.033","volume":"199","author":"Y.-D. Seo","year":"2010","unstructured":"Seo, Y.-D., Kim, H.-J., & Youn, S.-K. (2010). Isogeometric topology optimization using trimmed spline surfaces. Computer Methods in Applied Mechanics and Engineering, 199(49\u201352), 3270\u20133296.","journal-title":"Computer Methods in Applied Mechanics and Engineering"},{"issue":"1","key":"6_CR79","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s00466-015-1219-1","volume":"57","author":"Y. Wang","year":"2016","unstructured":"Wang, Y., & Benson, D. J. (2016). Isogeometric analysis for parameterized LSM-based structural topology optimization. Computational Mechanics, 57(1), 19\u201335.","journal-title":"Computational Mechanics"},{"issue":"7","key":"6_CR80","first-page":"1495","volume":"47","author":"F. Zhun","year":"2021","unstructured":"Zhun, F., Jie, Z. G., Ji, L. W., Gen, Y. Y., Ming, L. X., Han, L. P., & Bin, X. (2021). Applications of evolutionary computation in the design automation of complex mechatronic system: a survey. Acta Automatica Sinica, 47(7), 1495\u20131515.","journal-title":"Acta Automatica Sinica"},{"issue":"8\u20139","key":"6_CR81","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1016\/S0957-4158(03)00006-0","volume":"13","author":"K. Seo","year":"2003","unstructured":"Seo, K., Fan, Z., Hu, J., Goodman, E. D., & Rosenberg, R. C. (2003). Toward a unified and automated design methodology for multi-domain dynamic systems using bond graphs and genetic programming. Mechatronics, 13(8\u20139), 851\u2013885.","journal-title":"Mechatronics"},{"issue":"4","key":"6_CR82","doi-asserted-by":"publisher","DOI":"10.1115\/1.2885180","volume":"130","author":"Z. Wu","year":"2008","unstructured":"Wu, Z., Campbell, M. I., & Fern\u00e1ndez, B. R. (2008). Bond graph based automated modeling for computer-aided design of dynamic systems. Journal of Mechanical Design, 130(4), 041102.","journal-title":"Journal of Mechanical Design"},{"issue":"6","key":"6_CR83","doi-asserted-by":"publisher","first-page":"2681","DOI":"10.1007\/s00419-021-01914-4","volume":"91","author":"J. Li","year":"2021","unstructured":"Li, J., Wang, L., & Yan, B. (2021). Modeling and dynamic analysis of the dynamic stabilization unit based on bond graph. Archive of Applied Mechanics, 91(6), 2681\u20132695.","journal-title":"Archive of Applied Mechanics"},{"key":"6_CR84","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/978-3-642-18272-3_14","volume-title":"New horizons in evolutionary robotics","author":"M. T. Tolley","year":"2011","unstructured":"Tolley, M. T., Hiller, J. D., & Lipson, H. (2011). Evolutionary design and assembly planning for stochastic modular robots. In S. Doncieux, N.\u00a0Bred\u00e8che, & J.-B. Mouret (Eds.), New horizons in evolutionary robotics (pp.\u00a0211\u2013225). Berlin: Springer."},{"issue":"1","key":"6_CR85","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/MRA.2007.339623","volume":"14","author":"M. Yim","year":"2007","unstructured":"Yim, M., Shen, W.-M., Salemi, B., Rus, D., Moll, M., Lipson, H., Klavins, E., & Chirikjian, G. S. (2007). Modular self-reconfigurable robot systems [grand challenges of robotics]. IEEE Robotics & Automation Magazine, 14(1), 43\u201352.","journal-title":"IEEE Robotics & Automation Magazine"},{"key":"6_CR86","first-page":"161","volume-title":"Robotics: science and systems I","author":"P. White","year":"2005","unstructured":"White, P., Zykov, V., Bongard, J. C., & Lipson, H. (2005). Three dimensional stochastic reconfiguration of modular robots. In S. Thrun, G.\u00a0S. Sukhatme, & S. Schaal (Eds.), Robotics: science and systems I (pp.\u00a0161\u2013168). Cambridge: The MIT Press."},{"issue":"2","key":"6_CR87","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/s10514-006-8546-1","volume":"21","author":"E. H. \u00d8stergaard","year":"2006","unstructured":"\u00d8stergaard, E. H., Kassow, K., Beck, R., & Lund, H. H. (2006). Design of the ATRON lattice-based self-reconfigurable robot. Autonomous Robots, 21(2), 165\u2013183.","journal-title":"Autonomous Robots"},{"issue":"62","key":"6_CR88","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abk2822","volume":"7","author":"T. Miki","year":"2022","unstructured":"Miki, T., Lee, J., Hwangbo, J., Wellhausen, L., Koltun, V., & Hutter, M. (2022). Learning robust perceptive locomotion for quadrupedal robots in the wild. Science Robotics, 7(62), eabk2822.","journal-title":"Science Robotics"},{"issue":"58","key":"6_CR89","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abf2756","volume":"6","author":"I. Abad\u00eda","year":"2021","unstructured":"Abad\u00eda, I., Naveros, F., Ros, E., Carrillo, R. R., & Luque, N. R. (2021). A\u00a0cerebellar-based solution to the nondeterministic time delay problem in robotic control. Science Robotics, 6(58), eabf2756.","journal-title":"Science Robotics"},{"issue":"54","key":"6_CR90","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abg2133","volume":"6","author":"T. Chen","year":"2021","unstructured":"Chen, T., He, Z., & Ciocarlie, M. (2021). Co-designing hardware and control for robot hands. Science Robotics, 6(54), eabg2133.","journal-title":"Science Robotics"},{"key":"6_CR91","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1016\/j.rcim.2019.05.002","volume":"59","author":"H. Zhong","year":"2019","unstructured":"Zhong, H., Hu, C., Li, X., Gao, L., Zeng, B., & Dong, H. (2019). Kinematic calibration method for a two-segment hydraulic leg based on an improved whale swarm algorithm. Robotics and Computer-Integrated Manufacturing, 59, 361\u2013372.","journal-title":"Robotics and Computer-Integrated Manufacturing"},{"issue":"3","key":"6_CR92","doi-asserted-by":"publisher","first-page":"941","DOI":"10.1007\/s12555-021-0099-8","volume":"20","author":"H. Zhong","year":"2022","unstructured":"Zhong, H., Xie, S., Li, X., Gao, L., & Lu, S. (2022). Online gait generation method based on neural network for humanoid robot fast walking on uneven terrain. International Journal of Control, Automation, and Systems, 20(3), 941\u2013955.","journal-title":"International Journal of Control, Automation, and Systems"},{"issue":"2","key":"6_CR93","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1007\/s12555-020-0141-2","volume":"19","author":"H. Dong","year":"2021","unstructured":"Dong, H., Li, X., Shen, P., Gao, L., & Zhong, H. (2021). Interval type-2 fuzzy logic PID controller based on differential evolution with better and nearest option for hydraulic serial elastic actuator. International Journal of Control, Automation, and Systems, 19(2), 1113\u20131132.","journal-title":"International Journal of Control, Automation, and Systems"},{"key":"6_CR94","doi-asserted-by":"publisher","unstructured":"Dong, H., Gao, L., Shen, P., Li, X., Lu, Y., & Dai, W. (2019). An interval type-2 fuzzy logic controller design method for hydraulic actuators of a human-like robot by using improved drone squadron optimization. International Journal of Advanced Robotic Systems, 16(6). https:\/\/doi.org\/10.1177\/1729881419891553.","DOI":"10.1177\/1729881419891553"},{"issue":"6","key":"6_CR95","doi-asserted-by":"publisher","first-page":"2616","DOI":"10.1109\/TMECH.2019.2953239","volume":"24","author":"X. Hai","year":"2019","unstructured":"Hai, X., Wang, Z., Feng, Q., Ren, Y., Xu, B., Cui, J., & Duan, H. (2019). Mobile robot ADRC with an automatic parameter tuning mechanism via modified pigeon-inspired optimization. IEEE\/ASME Transactions on Mechatronics, 24(6), 2616\u20132626.","journal-title":"IEEE\/ASME Transactions on Mechatronics"},{"issue":"20","key":"6_CR96","doi-asserted-by":"publisher","first-page":"15771","DOI":"10.1007\/s00521-018-3514-1","volume":"32","author":"C. A. C\u00e1ceres Fl\u00f3rez","year":"2020","unstructured":"C\u00e1ceres Fl\u00f3rez, C. A., Ros\u00e1rio, J. M., & Amaya, D. (2020). Control structure for a car-like robot using artificial neural networks and genetic algorithms. Neural Computing & Applications, 32(20), 15771\u201315784.","journal-title":"Neural Computing & Applications"},{"issue":"2","key":"6_CR97","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1109\/TMECH.2018.2806389","volume":"23","author":"C. S. Chin","year":"2018","unstructured":"Chin, C. S., & Lin, W. P. (2018). Robust genetic algorithm and fuzzy inference mechanism embedded in a sliding-mode controller for an uncertain underwater robot. IEEE\/ASME Transactions on Mechatronics, 23(2), 655\u2013666.","journal-title":"IEEE\/ASME Transactions on Mechatronics"},{"key":"6_CR98","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.ymssp.2017.12.014","volume":"105","author":"H. Feng","year":"2018","unstructured":"Feng, H., Yin, C.-B., Weng, W., Ma, W., Zhou, J., Jia, W., & Zhang, Z. (2018). Robotic excavator trajectory control using an improved GA based PID controller. Mechanical Systems and Signal Processing, 105, 153\u2013168.","journal-title":"Mechanical Systems and Signal Processing"},{"issue":"9","key":"6_CR99","doi-asserted-by":"publisher","first-page":"4274","DOI":"10.1016\/j.eswa.2013.12.030","volume":"41","author":"R. Sharma","year":"2014","unstructured":"Sharma, R., Rana, K. P. S., & Kumar, V. (2014). Performance analysis of fractional order fuzzy PID controllers applied to a robotic manipulator. Expert Systems with Applications, 41(9), 4274\u20134289.","journal-title":"Expert Systems with Applications"},{"issue":"16","key":"6_CR100","doi-asserted-by":"publisher","first-page":"8","DOI":"10.5120\/17607-8016","volume":"100","author":"R. S. Ali","year":"2014","unstructured":"Ali, R. S., Aldair, A. A., & Almousawi, A. K. (2014). Design an optimal PID controller using artificial bee colony and genetic algorithm for autonomous mobile robot. International Journal of Computer Applications, 100(16), 8\u201316.","journal-title":"International Journal of Computer Applications"},{"issue":"1","key":"6_CR101","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/s11071-014-1661-1","volume":"79","author":"M. Taherkhorsandi","year":"2015","unstructured":"Taherkhorsandi, M., Mahmoodabadi, M. J., Talebipour, M., & Castillo-Villar, K. K. (2015). Pareto design of an adaptive robust hybrid of PID and sliding control for a biped robot via genetic algorithm optimization. Nonlinear Dynamics, 79(1), 251\u2013263.","journal-title":"Nonlinear Dynamics"},{"key":"6_CR102","doi-asserted-by":"publisher","first-page":"5599","DOI":"10.1109\/ChiCC.2015.7260514","volume-title":"2015 34th Chinese control conference (CCC)","author":"S. Zhenlu","year":"2015","unstructured":"Zhenlu, S., Bin, X., & Jie, C. (2015). Optimal design of controllers based on libraries and differential evolution. In 2015 34th Chinese control conference (CCC) (pp.\u00a05599\u20135604). Los Alamitos: IEEE."},{"key":"6_CR103","doi-asserted-by":"publisher","first-page":"2545","DOI":"10.23919\/ChiCC.2017.8027744","volume-title":"2017 36th Chinese control conference (CCC)","author":"Z. Jiaoyang","year":"2017","unstructured":"Jiaoyang, Z., Bin, X., & Jie, C. (2017). Evolutionary design of controllers with optimized structure and its application in a Maglev ball control system. In 2017 36th Chinese control conference (CCC) (pp.\u00a02545\u20132550). Los Alamitos: IEEE."},{"issue":"9","key":"6_CR104","doi-asserted-by":"publisher","first-page":"9655","DOI":"10.1109\/TIE.2021.3114700","volume":"69","author":"B. Xin","year":"2021","unstructured":"Xin, B., Wang, Y., Xue, W., Cai, T., Fan, Z., Zhan, J., & Chen, J. (2021). Evolution of controllers under a generalized structure encoding\/decoding scheme with application to magnetic levitation system. IEEE Transactions on Industrial Electronics, 69(9), 9655\u20139666.","journal-title":"IEEE Transactions on Industrial Electronics"},{"issue":"7","key":"6_CR105","doi-asserted-by":"publisher","first-page":"4091","DOI":"10.1109\/TSMC.2019.2933050","volume":"51","author":"S. Zhang","year":"2019","unstructured":"Zhang, S., Yang, P., Kong, L., Chen, W., Fu, Q., & Peng, K. (2019). Neural networks-based fault tolerant control of a robot via fast terminal sliding mode. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(7), 4091\u20134101.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"6_CR106","first-page":"63","volume-title":"Proceedings of the 22nd annual conference on computer graphics and interactive techniques","author":"R. Grzeszczuk","year":"1995","unstructured":"Grzeszczuk, R., & Terzopoulos, D. (1995). Automated learning of muscle-actuated locomotion through control abstraction. In S.\u00a0G. Mair & R. Cook (Eds.), Proceedings of the 22nd annual conference on computer graphics and interactive techniques (pp.\u00a063\u201370). New York: ACM."},{"issue":"3","key":"6_CR107","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1162\/106454602320991837","volume":"8","author":"G. S. Hornby","year":"2002","unstructured":"Hornby, G. S., & Pollack, J. B. (2002). Creating high-level components with a generative representation for body-brain evolution. Artificial Life, 8(3), 223\u2013246.","journal-title":"Artificial Life"},{"issue":"1","key":"6_CR108","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/S0921-8890(96)00036-X","volume":"19","author":"J. C. Gallagher","year":"1996","unstructured":"Gallagher, J. C., Beer, R. D., Espenschied, K. S., & Quinn, R. D. (1996). Application of evolved locomotion controllers to a hexapod robot. Robotics and Autonomous Systems, 19(1), 95\u2013103.","journal-title":"Robotics and Autonomous Systems"},{"key":"6_CR109","doi-asserted-by":"crossref","unstructured":"Floreano, D., Husbands, P., & Nolfi, S. (2008). Evolutionary robotics. Technical report, Berlin: Springer.","DOI":"10.1007\/978-3-540-30301-5_62"},{"key":"6_CR110","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1109\/IROS.2001.973363","volume-title":"Proceedings 2001 IEEE\/RSJ international conference on intelligent robots and systems. Expanding the societal role of robotics in the the next millennium (cat. no.01CH37180)","author":"C. Paul","year":"2001","unstructured":"Paul, C., & Bongard, J. C. (2001). The road less travelled: morphology in the optimization of biped robot locomotion. In Proceedings 2001 IEEE\/RSJ international conference on intelligent robots and systems. Expanding the societal role of robotics in the the next millennium (cat. no.01CH37180) (Vol.\u00a01, pp.\u00a0226\u2013232). Los Alamitos: IEEE. https:\/\/doi.org\/10.1109\/IROS.2001.973363."},{"issue":"10","key":"6_CR111","doi-asserted-by":"publisher","first-page":"2045","DOI":"10.1177\/1077546316676734","volume":"24","author":"M. Rahmani","year":"2018","unstructured":"Rahmani, M., Ghanbari, A., & Ettefagh, M. M. (2018). A novel adaptive neural network integral sliding-mode control of a biped robot using bat algorithm. Journal of Vibration and Control, 24(10), 2045\u20132060.","journal-title":"Journal of Vibration and Control"},{"issue":"1","key":"6_CR112","doi-asserted-by":"publisher","DOI":"10.4249\/scholarpedia.1463","volume":"9","author":"M. Dorigo","year":"2014","unstructured":"Dorigo, M., Birattari, M., & Brambilla, M. (2014). Swarm robotics. Scholarpedia, 9(1), 1463.","journal-title":"Scholarpedia"},{"issue":"12","key":"6_CR113","doi-asserted-by":"publisher","first-page":"13521","DOI":"10.1109\/TCYB.2021.3080044","volume":"52","author":"G. Gao","year":"2022","unstructured":"Gao, G., Mei, Y., Xin, B., Jia, Y.-H., & Browne, W. N. (2022). Automated coordination strategy design using genetic programming for dynamic multipoint dynamic aggregation. IEEE Transactions on Cybernetics, 52(12), 13521\u201313535. https:\/\/doi.org\/10.1109\/TCYB.2021.3080044.","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"4","key":"6_CR114","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1108\/17563780911005836","volume":"2","author":"S. Kazadi","year":"2009","unstructured":"Kazadi, S. (2009). Model independence in swarm robotics. International Journal of Intelligent Computing and Cybernetics, 2(4), 672\u2013694.","journal-title":"International Journal of Intelligent Computing and Cybernetics"},{"key":"6_CR115","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1109\/ICRA.2011.5980440","volume-title":"2011 IEEE international conference on robotics and automation","author":"S. Berman","year":"2011","unstructured":"Berman, S., Kumar, V., & Nagpal, R. (2011). Design of control policies for spatially inhomogeneous robot swarms with application to commercial pollination. In 2011 IEEE international conference on robotics and automation (pp.\u00a0378\u2013385). Los Alamitos: IEEE."},{"key":"6_CR116","first-page":"139","volume-title":"Proceedings of the 11th international conference on autonomous agents and multiagent systems","author":"M. Brambilla","year":"2012","unstructured":"Brambilla, M., Pinciroli, C., Birattari, M., & Dorigo, M. (2012). Property-driven design for swarm robotics. In V. Conitzer & M. Winikoff (Eds.), Proceedings of the 11th international conference on autonomous agents and multiagent systems (Vol.\u00a01, pp.\u00a0139\u2013146). IFAAMAS."},{"issue":"2","key":"6_CR117","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/s11721-014-0092-4","volume":"8","author":"G. Francesca","year":"2014","unstructured":"Francesca, G., Brambilla, M., Brutschy, A., Trianni, V., & Birattari, M. (2014). AutoMoDe: a novel approach to the automatic design of control software for robot swarms. Swarm Intelligence, 8(2), 89\u2013112.","journal-title":"Swarm Intelligence"},{"issue":"2","key":"6_CR118","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s11721-015-0107-9","volume":"9","author":"G. Francesca","year":"2015","unstructured":"Francesca, G., Brambilla, M., Brutschy, A., Garattoni, L., Miletitch, R., Podevijn, G., et al. (2015). AutoMoDe-Chocolate: automatic design of control software for robot swarms. Swarm Intelligence, 9(2), 125\u2013152.","journal-title":"Swarm Intelligence"},{"issue":"7","key":"6_CR119","doi-asserted-by":"publisher","first-page":"1152","DOI":"10.1109\/JPROC.2021.3072740","volume":"109","author":"M. Dorigo","year":"2021","unstructured":"Dorigo, M., Theraulaz, G., & Trianni, V. (2021). Swarm robotics: past, present, and future [point of view]. Proceedings of the IEEE, 109(7), 1152\u20131165.","journal-title":"Proceedings of the IEEE"},{"key":"6_CR120","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.robot.2016.10.008","volume":"90","author":"E. Nunes","year":"2017","unstructured":"Nunes, E., Manner, M., Mitiche, H., & Gini, M. (2017). A taxonomy for task allocation problems with temporal and ordering constraints. Robotics and Autonomous Systems, 90, 55\u201370.","journal-title":"Robotics and Autonomous Systems"},{"key":"6_CR121","doi-asserted-by":"publisher","DOI":"10.1016\/j.jii.2021.100239","volume":"25","author":"M. Wu","year":"2022","unstructured":"Wu, M., Zhu, X., Ma, L., Wang, J., Bao, W., Li, W., & Fan, Z. (2022). Torch: strategy evolution in swarm robots using heterogeneous\u2013homogeneous coevolution method. Journal of Industrial Information Integration, 25, 100239.","journal-title":"Journal of Industrial Information Integration"},{"issue":"20","key":"6_CR122","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.aat3536","volume":"3","author":"G. V\u00e1s\u00e1rhelyi","year":"2018","unstructured":"V\u00e1s\u00e1rhelyi, G., Vir\u00e1gh, C., Somorjai, G., Nepusz, T., Eiben, A. E., & Vicsek, T. (2018). Optimized flocking of autonomous drones in confined environments. Science Robotics, 3(20), eaat3536.","journal-title":"Science Robotics"},{"issue":"5853","key":"6_CR123","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1126\/science.1145803","volume":"318","author":"R. Pfeifer","year":"2007","unstructured":"Pfeifer, R., Lungarella, M., & Iida, F. (2007). Self-organization, embodiment, and biologically inspired robotics. Science, 318(5853), 1088\u20131093.","journal-title":"Science"},{"issue":"19","key":"6_CR124","doi-asserted-by":"publisher","DOI":"10.1002\/adma.202002882","volume":"33","author":"D. Shah","year":"2021","unstructured":"Shah, D., Yang, B., Kriegman, S., Levin, M., Bongard, J., & Kramer-Bottiglio, R. (2021). Shape changing robots: bioinspiration, simulation, and physical realization. Advanced Materials, 33(19), 2002882.","journal-title":"Advanced Materials"},{"issue":"5","key":"6_CR125","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0233848","volume":"15","author":"K. Miras","year":"2020","unstructured":"Miras, K., Ferrante, E., & Eiben, A. E. (2020). Environmental influences on evolvable robots. PLoS ONE, 15(5), e0233848.","journal-title":"PLoS ONE"},{"key":"6_CR126","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1007\/978-3-319-99253-2_38","volume-title":"International conference on parallel problem solving from nature","author":"G. Lan","year":"2018","unstructured":"Lan, G., Jelisavcic, M., Roijers, D. M., Haasdijk, E., & Eiben, A. E. (2018). Directed locomotion for modular robots with evolvable morphologies. In International conference on parallel problem solving from nature (pp.\u00a0476\u2013487). Berlin: Springer."},{"key":"6_CR127","first-page":"1056","volume-title":"European conference on artificial life (ECAL-2013)","author":"A. E. Eiben","year":"2013","unstructured":"Eiben, A. E., Bredeche, N., Hoogendoorn, M., Stradner, J., Timmis, J., Tyrrell, A., & Winfield, A. (2013). The triangle of life: evolving robots in real-time and real-space. In European conference on artificial life (ECAL-2013) (pp.\u00a01056\u20131063). Cambridge: The MIT Press."},{"issue":"7553","key":"6_CR128","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1038\/nature14544","volume":"521","author":"A. E. Eiben","year":"2015","unstructured":"Eiben, A. E., & Smith, J. (2015). From evolutionary computation to the evolution of things. Nature, 521(7553), 476\u2013482.","journal-title":"Nature"},{"issue":"4","key":"6_CR129","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s12065-012-0071-x","volume":"5","author":"A. E. Eiben","year":"2012","unstructured":"Eiben, A. E., Kernbach, S., & Haasdijk, E. (2012). Embodied artificial evolution. Evolutionary Intelligence, 5(4), 261\u2013272.","journal-title":"Evolutionary Intelligence"},{"issue":"8","key":"6_CR130","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1145\/2493883","volume":"56","author":"J. C. Bongard","year":"2013","unstructured":"Bongard, J. C. (2013). Evolutionary robotics. Communications of the ACM, 56(8), 74\u201383.","journal-title":"Communications of the ACM"},{"key":"6_CR131","first-page":"712","volume-title":"Proceedings of the eighth conference on intelligent autonomous systems (IAS8)","author":"D. Marbach","year":"2004","unstructured":"Marbach, D., & Ijspeert, A. J. (2004). Co-evolution of configuration and control for homogenous modular robots. In Proceedings of the eighth conference on intelligent autonomous systems (IAS8) (pp.\u00a0712\u2013719). IOS Press."},{"issue":"1","key":"6_CR132","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-021-25874-z","volume":"12","author":"A. Gupta","year":"2021","unstructured":"Gupta, A., Savarese, S., Ganguli, S., & Fei-Fei, L. (2021). Embodied intelligence via learning and evolution. Nature Communications, 12(1), 1\u201312.","journal-title":"Nature Communications"},{"key":"6_CR133","unstructured":"Schaff, C. (2022). Neural approaches to co-optimization in robotics. arXiv preprint arXiv:2209.00579."},{"key":"6_CR134","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/978-3-7908-1882-6_9","volume-title":"Soft computing for intelligent robotic systems","author":"L. Meeden","year":"1998","unstructured":"Meeden, L., & Kumar, D. (1998). Trends in evolutionary robotics. In L.\u00a0C. Jain & T. Fukuda (Eds.), Soft computing for intelligent robotic systems (pp.\u00a0215\u2013233). Berlin: Springer."},{"issue":"6","key":"6_CR135","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3414685.3417831","volume":"39","author":"A. Zhao","year":"2020","unstructured":"Zhao, A., Xu, J., Konakovi\u0107-Lukovi\u0107, M., Hughes, J., Spielberg, A., Rus, D., & Matusik, W. (2020). Robogrammar: graph grammar for terrain-optimized robot design. ACM Transactions on Graphics, 39(6), 1\u201316.","journal-title":"ACM Transactions on Graphics"},{"key":"6_CR136","doi-asserted-by":"publisher","first-page":"9863","DOI":"10.1109\/ICRA48506.2021.9561818","volume-title":"2021 IEEE international conference on robotics and automation (ICRA)","author":"J. Xu","year":"2021","unstructured":"Xu, J., Spielberg, A., Zhao, A., Rus, D., & Matusik, W. (2021). Multi-objective graph heuristic search for terrestrial robot design. In 2021 IEEE international conference on robotics and automation (ICRA) (pp.\u00a09863\u20139869). Los Alamitos: IEEE."},{"issue":"11","key":"6_CR137","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1038\/s42256-020-00258-y","volume":"2","author":"A. Miriyev","year":"2020","unstructured":"Miriyev, A., & Kova\u010d, M. (2020). Skills for physical artificial intelligence. Nature Machine Intelligence, 2(11), 658\u2013660.","journal-title":"Nature Machine Intelligence"},{"issue":"3","key":"6_CR138","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1017\/S0890060408000152","volume":"22","author":"J. Wang","year":"2008","unstructured":"Wang, J., Fan, Z., Terpenny, J. P., & Goodman, E. D. (2008). Cooperative body\u2013brain coevolutionary synthesis of mechatronic systems. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 22(3), 219\u2013234.","journal-title":"Artificial Intelligence for Engineering Design, Analysis and Manufacturing"},{"issue":"2","key":"6_CR139","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1080\/00207721.2013.783643","volume":"46","author":"J.-F. Dupuis","year":"2015","unstructured":"Dupuis, J.-F., Fan, Z., & Goodman, E. (2015). Evolutionary design of discrete controllers for hybrid mechatronic systems. International Journal of Systems Science, 46(2), 303\u2013316.","journal-title":"International Journal of Systems Science"},{"issue":"2","key":"6_CR140","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1109\/MIM.2022.9756392","volume":"25","author":"H. Zhang","year":"2022","unstructured":"Zhang, H., Liu, L. Z., Xie, H., Jiang, Y., Zhou, J., & Wang, Y. (2022). Deep learning-based robot vision: high-end tools for smart manufacturing. IEEE Instrumentation & Measurement Magazine, 25(2), 27\u201335.","journal-title":"IEEE Instrumentation & Measurement Magazine"},{"issue":"9","key":"6_CR141","doi-asserted-by":"publisher","first-page":"2891","DOI":"10.1109\/TPAMI.2020.3020300","volume":"43","author":"X. Zhang","year":"2021","unstructured":"Zhang, X., Huang, Z., Wang, N., Xiang, S., & Pan, C. (2021). You only search once: single shot neural architecture search via direct sparse optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 2891\u20132904.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"9","key":"6_CR142","doi-asserted-by":"publisher","first-page":"2936","DOI":"10.1109\/TPAMI.2021.3065138","volume":"43","author":"X. Zheng","year":"2021","unstructured":"Zheng, X., Ji, R., Chen, Y., Wang, Q., Zhang, B., Chen, J., Ye, Q., Huang, F., & Tian, Y. (2021). MIGO-NAS: towards fast and generalizable neural architecture search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 2936\u20132952.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"6_CR143","first-page":"3825","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Y. Xiong","year":"2021","unstructured":"Xiong, Y., Liu, H., Gupta, S., Akin, B., Bender, G., Wang, Y., et al. (2021). Mobiledets: searching for object detection architectures for mobile accelerators. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a03825\u20133834). Los Alamitos: IEEE."},{"key":"6_CR144","first-page":"2820","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"M. Tan","year":"2019","unstructured":"Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., & Le, Q. V. (2019). MnasNet: platform-aware neural architecture search for mobile. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a02820\u20132828). Los Alamitos: IEEE."},{"key":"6_CR145","doi-asserted-by":"publisher","first-page":"950","DOI":"10.1109\/CEC45853.2021.9504890","volume-title":"2021 IEEE congress on evolutionary computation (CEC)","author":"X. Zhou","year":"2021","unstructured":"Zhou, X., Qin, A. K., Sun, Y., & Tan, K. C. (2021). A survey of advances in evolutionary neural architecture search. In 2021 IEEE congress on evolutionary computation (CEC) (pp.\u00a0950\u2013957). Los Alamitos: IEEE."},{"key":"6_CR146","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.neucom.2021.12.014","volume":"474","author":"D. Baymurzina","year":"2022","unstructured":"Baymurzina, D., Golikov, E., & Burtsev, M. (2022). A review of neural architecture search. Neurocomputing, 474, 82\u201393.","journal-title":"Neurocomputing"},{"issue":"1","key":"6_CR147","first-page":"1997","volume":"20","author":"T. Elsken","year":"2019","unstructured":"Elsken, T., Metzen, J. H., & Hutter, F. (2019). Neural architecture search: a survey. Journal of Machine Learning Research, 20(1), 1997\u20132017.","journal-title":"Journal of Machine Learning Research"},{"key":"6_CR148","unstructured":"Baker, B., Gupta, O., Naik, N., & Raskar, R. (2016). Designing neural network architectures using reinforcement learning. arXiv preprint arXiv:1611.02167."},{"key":"6_CR149","unstructured":"Zoph, B., & Le, Q. V. (2016). Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578."},{"key":"6_CR150","first-page":"8697","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"B. Zoph","year":"2018","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (2018). Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a08697\u20138710). Los Alamitos: IEEE."},{"key":"6_CR151","first-page":"2423","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Z. Zhong","year":"2018","unstructured":"Zhong, Z., Yan, J., Wu, W., Shao, J., & Liu, C.-L. (2018). Practical block-wise neural network architecture generation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a02423\u20132432). Los Alamitos: IEEE."},{"key":"6_CR152","unstructured":"Liu, H., Simonyan, K., & Yang, Y. (2018). Darts: differentiable architecture search. arXiv preprint arXiv:1806.09055."},{"key":"6_CR153","first-page":"1294","volume-title":"Proceedings of the IEEE international conference on computer vision","author":"X. Chen","year":"2019","unstructured":"Chen, X., Xie, L., Wu, J., & Tian, Q. (2019). Progressive differentiable architecture search: bridging the depth gap between search and evaluation. In Proceedings of the IEEE international conference on computer vision (pp.\u00a01294\u20131303). Los Alamitos: IEEE."},{"issue":"1","key":"6_CR154","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s42256-018-0006-z","volume":"1","author":"K. O. Stanley","year":"2019","unstructured":"Stanley, K. O., Clune, J., Lehman, J., & Miikkulainen, R. (2019). Designing neural networks through neuroevolution. Nature Machine Intelligence, 1(1), 24\u201335.","journal-title":"Nature Machine Intelligence"},{"issue":"2","key":"6_CR155","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1162\/106365602320169811","volume":"10","author":"K. O. Stanley","year":"2002","unstructured":"Stanley, K. O., & Miikkulainen, R. (2002). Evolving neural networks through augmenting topologies. Evolutionary Computation, 10(2), 99\u2013127.","journal-title":"Evolutionary Computation"},{"key":"6_CR156","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/B978-0-12-815480-9.00015-3","volume-title":"Artificial intelligence in the age of neural networks and brain computing","author":"R. Miikkulainen","year":"2019","unstructured":"Miikkulainen, R., Liang, J., Meyerson, E., Rawal, A., Fink, D., Francon, O., et al. (2019). Evolving deep neural networks. In R. Kozma, C. Alippi, Y. Choe, et al. (Eds.), Artificial intelligence in the age of neural networks and brain computing (pp.\u00a0293\u2013312). Amsterdam: Elsevier."},{"key":"6_CR157","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1145\/3321707.3321729","volume-title":"Proceedings of the genetic and evolutionary computation conference","author":"Z. Lu","year":"2019","unstructured":"Lu, Z., Whalen, I., Boddeti, V., Dhebar, Y., Deb, K., Goodman, E., & Banzhaf, W. (2019). NSGA-NET: neural architecture search using multi-objective genetic algorithm. In Proceedings of the genetic and evolutionary computation conference (pp.\u00a0419\u2013427). New York: ACM."},{"key":"6_CR158","first-page":"779","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"J. Redmon","year":"2016","unstructured":"Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a0779\u2013788). Los Alamitos: IEEE."},{"key":"6_CR159","first-page":"8844","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"T. Meinhardt","year":"2022","unstructured":"Meinhardt, T., Kirillov, A., Leal-Taixe, L., & Feichtenhofer, C. (2022). Trackformer: multi-object tracking with transformers. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a08844\u20138854). Los Alamitos: IEEE."},{"key":"6_CR160","doi-asserted-by":"publisher","first-page":"9107","DOI":"10.1109\/ICRA46639.2022.9812253","volume-title":"2022 international conference on robotics and automation (ICRA)","author":"T. Gilles","year":"2022","unstructured":"Gilles, T., Sabatini, S., Tsishkou, D., Stanciulescu, B., & Moutarde, F. (2022). GOHOME: graph-oriented heatmap output for future motion estimation. In 2022 international conference on robotics and automation (ICRA) (pp.\u00a09107\u20139114). Los Alamitos: IEEE."},{"issue":"10","key":"6_CR161","doi-asserted-by":"publisher","first-page":"2410","DOI":"10.1109\/TPAMI.2019.2936024","volume":"42","author":"R. Ji","year":"2019","unstructured":"Ji, R., Li, K., Wang, Y., Sun, X., Guo, F., Guo, X., Wu, Y., Huang, F., & Luo, J. (2019). Semi-supervised adversarial monocular depth estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(10), 2410\u20132422.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"4","key":"6_CR162","doi-asserted-by":"publisher","first-page":"3876","DOI":"10.1109\/TIE.2021.3075836","volume":"69","author":"H. Zhang","year":"2021","unstructured":"Zhang, H., Liang, Z., Li, C., Zhong, H., Liu, L., Zhao, C., Wang, Y., & Wu, Q. J. (2021). A practical robotic grasping method by using 6-d pose estimation with protective correction. IEEE Transactions on Industrial Electronics, 69(4), 3876\u20133886.","journal-title":"IEEE Transactions on Industrial Electronics"},{"issue":"1","key":"6_CR163","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-15341-0","volume":"12","author":"A. Gupta","year":"2022","unstructured":"Gupta, A., Sheth, P., & Xie, P. (2022). Neural architecture search for pneumonia diagnosis from chest X-rays. Scientific Reports, 12(1), Article No. 11309.","journal-title":"Scientific Reports"},{"issue":"1","key":"6_CR164","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-98978-7","volume":"11","author":"O. N. Oyelade","year":"2021","unstructured":"Oyelade, O. N., & Ezugwu, A. E. (2021). A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images. Scientific Reports, 11(1), Article No. 19940.","journal-title":"Scientific Reports"},{"issue":"3","key":"6_CR165","doi-asserted-by":"publisher","first-page":"1032","DOI":"10.1109\/TMI.2020.3045295","volume":"40","author":"Y. Chen","year":"2021","unstructured":"Chen, Y., Zhang, H., Wang, Y., Yang, Y., Zhou, X., & Wu, Q. M. J. (2021). MAMA Net: multi-scale attention memory autoencoder network for anomaly detection. IEEE Transactions on Medical Imaging, 40(3), 1032\u20131041. https:\/\/doi.org\/10.1109\/TMI.2020.3045295.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"6_CR166","first-page":"740","volume-title":"European conference on computer vision","author":"T.-Y. Lin","year":"2014","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., & Zitnick, C. L. (2014). Microsoft COCO: common objects in context. In European conference on computer vision (pp.\u00a0740\u2013755). Berlin: Springer."},{"key":"6_CR167","first-page":"3828","volume-title":"Proceedings of the IEEE international conference on computer vision","author":"C. Godard","year":"2019","unstructured":"Godard, C., Mac Aodha, O., Firman, M., & Brostow, G. J. (2019). Digging into self-supervised monocular depth estimation. In Proceedings of the IEEE international conference on computer vision (pp.\u00a03828\u20133838). Los Alamitos: IEEE."},{"key":"6_CR168","first-page":"367","volume-title":"European conference on computer vision","author":"Y. Wang","year":"2020","unstructured":"Wang, Y., Song, Y., Ma, C., & Zeng, B. (2020). Rethinking image deraining via rain streaks and vapors. In European conference on computer vision (pp.\u00a0367\u2013382). Berlin: Springer."},{"key":"6_CR169","doi-asserted-by":"crossref","unstructured":"Ren, W., Zhou, L., & Chen, J. (2022). Unsupervised single image dehazing with generative adversarial network. Multimedia Systems, 1\u201311.","DOI":"10.1007\/s00530-021-00852-z"},{"issue":"1","key":"6_CR170","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1109\/TII.2018.2843811","volume":"15","author":"J. Wan","year":"2018","unstructured":"Wan, J., Tang, S., Li, d., Imran, M., Zhang, C., Liu, C., & Pang, Z. (2018). Reconfigurable smart factory for drug packing in healthcare industry 4.0. IEEE Transactions on Industrial Informatics, 15(1), 507\u2013516.","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"6_CR171","first-page":"4365","volume-title":"2016 IEEE international conference on robotics and automation (ICRA)","author":"Q. Ma","year":"2016","unstructured":"Ma, Q., Li, H., & Chirikjian, G. S. (2016). New probabilistic approaches to the AX=XB hand-eye calibration without correspondence. In 2016 IEEE international conference on robotics and automation (ICRA) (pp.\u00a04365\u20134371). Los Alamitos: IEEE."},{"key":"6_CR172","doi-asserted-by":"publisher","first-page":"5402","DOI":"10.1109\/CAC53003.2021.9728631","volume-title":"2021 China automation congress (CAC)","author":"C. Niu","year":"2021","unstructured":"Niu, C., Zhu, Q., Wang, Y., Zhou, X., & Shen, W. (2021). Real time counting system of glass bottle based on multi objects tracking. In 2021 China automation congress (CAC) (pp.\u00a05402\u20135407). Los Alamitos: IEEE."},{"key":"6_CR173","first-page":"15180","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"B. Yan","year":"2021","unstructured":"Yan, B., Peng, H., Wu, K., Wang, D., Fu, J., & Lu, H. (2021). LightTrack: finding lightweight neural networks for object tracking via one-shot architecture search. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a015180\u201315189). Los Alamitos: IEEE."},{"issue":"1","key":"6_CR174","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-07898-7","volume":"12","author":"T. Viriyasaranon","year":"2022","unstructured":"Viriyasaranon, T., & Choi, J.-H. (2022). Object detectors involving a NAS-gate convolutional module and capsule attention module. Scientific Reports, 12(1), Article No. 3916.","journal-title":"Scientific Reports"},{"key":"6_CR175","first-page":"11943","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"N. Wang","year":"2020","unstructured":"Wang, N., Gao, Y., Chen, H., Wang, P., Tian, Z., Shen, C., & Zhang, Y. (2020). NAS-FCOS: fast neural architecture search for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a011943\u201311951). Los Alamitos: IEEE."},{"key":"6_CR176","first-page":"6642","volume-title":"Advances in neural information processing systems 32","author":"Y. Chen","year":"2019","unstructured":"Chen, Y., Yang, T., Zhang, X., Meng, G., Xiao, X., & Sun, J. (2019). DetNAS: backbone search for object detection. In H. Wallach, H. Larochelle, A.\u00a0Beygelzimer, et al. (Eds.), Advances in neural information processing systems 32 (pp.\u00a06642\u20136652). Red Hook: Curran Associates."},{"issue":"7","key":"6_CR177","doi-asserted-by":"publisher","first-page":"12661","DOI":"10.1609\/aaai.v34i07.6958","volume":"34","author":"L. Yao","year":"2020","unstructured":"Yao, L., Xu, H., Zhang, W., Liang, X., & Li, Z. (2020). SM-NAS: structural-to-modular neural architecture search for object detection. Proceedings of the AAAI Conference on Artificial Intelligence, 34(7), 12661\u201312668. Menlo Park: AAAI Press.","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"6_CR178","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3162615","volume":"71","author":"H. Zhang","year":"2022","unstructured":"Zhang, H., Wu, L., Chen, Y., Chen, R., Kong, S., Wang, Y., Hu, J., & Wu, J. (2022). Attention-guided multitask convolutional neural network for power line parts detection. IEEE Transactions on Instrumentation and Measurement, 71, 1\u201313. https:\/\/doi.org\/10.1109\/TIM.2022.3162615.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"6_CR179","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep learning in medical image analysis and multimodal learning for clinical decision support","author":"Z. Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. (2018). Unet++: a nested u-net architecture for medical image segmentation. In D. Stoyanov, Z. Taylor, G. Carneiro, et al. (Eds.), Deep learning in medical image analysis and multimodal learning for clinical decision support (pp.\u00a03\u201311). Berlin: Springer."},{"issue":"2","key":"6_CR180","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F. Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). NNU-NET: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203\u2013211.","journal-title":"Nature Methods"},{"key":"6_CR181","first-page":"82","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"C. Liu","year":"2019","unstructured":"Liu, C., Chen, L.-C., Schroff, F., Adam, H., Hua, W., Yuille, A. L., & Fei-Fei, L. (2019). Auto-deeplab: hierarchical neural architecture search for semantic image segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a082\u201392). Los Alamitos: IEEE."},{"key":"6_CR182","first-page":"9126","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"V. Nekrasov","year":"2019","unstructured":"Nekrasov, V., Chen, H., Shen, C., & Reid, I. (2019). Fast neural architecture search of compact semantic segmentation models via auxiliary cells. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a09126\u20139135). Los Alamitos: IEEE."},{"issue":"2","key":"6_CR183","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1109\/TMI.2021.3111679","volume":"41","author":"J. Wei","year":"2021","unstructured":"Wei, J., Zhu, G., Fan, Z., Liu, J., Rong, Y., Mo, J., Li, W., & Chen, X. (2021). Genetic U-Net: automatically designed deep networks for retinal vessel segmentation using a genetic algorithm. IEEE Transactions on Medical Imaging, 41(2), 292\u2013307.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"6_CR184","first-page":"3517","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"F. Zhang","year":"2019","unstructured":"Zhang, F., Zhu, X., & Ye, M. (2019). Fast human pose estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a03517\u20133526). Los Alamitos: IEEE."},{"key":"6_CR185","unstructured":"Milan, A., Leal-Taix\u00e9, L., Reid, I., Roth, S., & Schindler, K. (2016). MOT16: a benchmark for multi-object tracking. arXiv preprint arXiv:1603.00831."},{"key":"6_CR186","unstructured":"Zou, Z., Shi, Z., Guo, Y., & Ye, J. (2019). Object detection in 20 years: a survey. arXiv preprint arXiv:1905.05055."},{"key":"6_CR187","first-page":"270","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"C. Godard","year":"2017","unstructured":"Godard, C., Mac Aodha, O., & Brostow, G. J. (2017). Unsupervised monocular depth estimation with left-right consistency. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a0270\u2013279). Los Alamitos: IEEE."},{"key":"6_CR188","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1109\/ICSCC51209.2021.9528162","volume-title":"2021 8th international conference on smart computing and communications (ICSCC)","author":"V. R. Nagarajan","year":"2021","unstructured":"Nagarajan, V. R., & Singh, P. (2021). Obstacle detection and avoidance for mobile robots using monocular vision. In 2021 8th international conference on smart computing and communications (ICSCC) (pp.\u00a0275\u2013279). Los Alamitos: IEEE. https:\/\/doi.org\/10.1109\/ICSCC51209.2021.9528162."},{"issue":"6","key":"6_CR189","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1109\/70.736780","volume":"14","author":"I. Ohya","year":"1998","unstructured":"Ohya, I., Kosaka, A., & Kak, A. (1998). Vision-based navigation by a mobile robot with obstacle avoidance using single-camera vision and ultrasonic sensing. IEEE Transactions on Robotics and Automation, 14(6), 969\u2013978.","journal-title":"IEEE Transactions on Robotics and Automation"},{"issue":"5","key":"6_CR190","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TITS.2015.2430896","volume":"16","author":"T. Cao","year":"2015","unstructured":"Cao, T., Xiang, Z.-Y., & Liu, J.-L. (2015). Perception in disparity: an efficient navigation framework for autonomous vehicles with stereo cameras. IEEE Transactions on Intelligent Transportation Systems, 16(5), 2935\u20132948. https:\/\/doi.org\/10.1109\/TITS.2015.2430896.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"1","key":"6_CR191","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1109\/TRO.2021.3083197","volume":"38","author":"S. Song","year":"2021","unstructured":"Song, S., Kim, D., & Choi, S. (2021). View path planning via online multiview stereo for 3-D modeling of large-scale structures. IEEE Transactions on Robotics, 38(1), 372\u2013390.","journal-title":"IEEE Transactions on Robotics"},{"key":"6_CR192","first-page":"3643","volume-title":"Proceedings of the IEEE winter conference on applications of computer vision","author":"L. Huynh","year":"2022","unstructured":"Huynh, L., Nguyen, P., Matas, J., Rahtu, E., & Heikkil\u00e4, J. (2022). Lightweight monocular depth with a novel neural architecture search method. In Proceedings of the IEEE winter conference on applications of computer vision (pp.\u00a03643\u20133653). Los Alamitos: IEEE."},{"key":"6_CR193","first-page":"1812","volume-title":"Proceedings of the IEEE international conference on computer vision","author":"T. Saikia","year":"2019","unstructured":"Saikia, T., Marrakchi, Y., Zela, A., Hutter, F., & Brox, T. (2019). Autodispnet: Improving disparity estimation with automl. In Proceedings of the IEEE international conference on computer vision (pp.\u00a01812\u20131823). Los Alamitos: IEEE."},{"key":"6_CR194","doi-asserted-by":"publisher","first-page":"812","DOI":"10.1109\/TIP.2021.3135485","volume":"31","author":"K. Zeng","year":"2021","unstructured":"Zeng, K., Wang, Y., Mao, J., Liu, C., Peng, W., & Yang, Y. (2021). Deep stereo matching with hysteresis attention and supervised cost volume construction. IEEE Transactions on Image Processing, 31, 812\u2013822.","journal-title":"IEEE Transactions on Image Processing"},{"key":"6_CR195","first-page":"22158","volume-title":"Advances in Neural Information Processing Systems 33","author":"X. Cheng","year":"2020","unstructured":"Cheng, X., Zhong, Y., Harandi, M., Dai, Y., Chang, X., Li, H., Drummond, T., & Ge, Z. (2020). Hierarchical neural architecture search for deep stereo matching. In H. Larochelle, M. Ranzato, R. Hadsell, et al. (Eds.), Advances in Neural Information Processing Systems 33 (pp.\u00a022158\u201322169). Red Hook: Curran Associates."},{"key":"6_CR196","first-page":"18901","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"C. Zhang","year":"2022","unstructured":"Zhang, C., Tian, K., Fan, B., Meng, G., Zhang, Z., & Pan, C. (2022). Continual stereo matching of continuous driving scenes with growing architecture. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a018901\u201318910). Los Alamitos: IEEE."},{"key":"6_CR197","doi-asserted-by":"crossref","unstructured":"Wang, Q., Shi, S., Zhao, K., & Chu, X. (2022). EASNet: searching elastic and accurate network architecture for stereo matching. arXiv preprint arXiv:2207.09796.","DOI":"10.1007\/978-3-031-19824-3_26"},{"key":"6_CR198","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1109\/ICIP.2019.8802919","volume-title":"2019 IEEE international conference on image processing (ICIP)","author":"W. Peng","year":"2019","unstructured":"Peng, W., Hong, X., & Zhao, G. (2019). Video action recognition via neural architecture searching. In 2019 IEEE international conference on image processing (ICIP) (pp.\u00a011\u201315). Los Alamitos: IEEE."},{"issue":"1","key":"6_CR199","doi-asserted-by":"publisher","DOI":"10.1002\/ail2.38","volume":"3","author":"A. J. Piergiovanni","year":"2022","unstructured":"Piergiovanni, A. J., Angelova, A., & Ryoo, M. S. (2022). Tiny video networks. Applied AI Letters, 3(1), e38.","journal-title":"Applied AI Letters"},{"key":"6_CR200","unstructured":"Ryoo, M. S., Piergiovanni, A. J., Tan, M., & Angelova, A. (2019). AssembleNET: searching for multi-stream neural connectivity in video architectures. arXiv preprint arXiv:1905.13209."},{"key":"6_CR201","first-page":"449","volume-title":"European conference on computer vision","author":"X. Wang","year":"2020","unstructured":"Wang, X., Xiong, X., Neumann, M., Piergiovanni, A. J., Ryoo, M. S., Angelova, A., Kitani, K. M., & Hua, W. (2020). AttentionNAS: spatiotemporal attention cell search for video classification. In European conference on computer vision (pp.\u00a0449\u2013465). Berlin: Springer."},{"key":"6_CR202","doi-asserted-by":"crossref","unstructured":"Piergiovanni, A. J., Angelova, A., & Ryoo, M. (2020). Tiny video networks: architecture search for efficient video models. [Paper presentation]. In ICML workshop on automated machine learning (AutoML). http:\/\/icml2020.automl.org.","DOI":"10.22541\/au.162687236.67581007\/v1"},{"key":"6_CR203","first-page":"2480","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"S. Liu","year":"2021","unstructured":"Liu, S., Zheng, C., Lu, K., Gao, S., Wang, N., Wang, B., Zhang, D., Zhang, X., & Xu, T. (2021). Evsrnet: efficient video super-resolution with neural architecture search. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a02480\u20132485). Los Alamitos: IEEE."},{"key":"6_CR204","first-page":"16072","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"L. Xu","year":"2021","unstructured":"Xu, L., Guan, Y., Jin, S., Liu, W., Qian, C., Luo, P., Ouyang, W., & Wang, X. (2021). Vipnas: efficient video pose estimation via neural architecture search. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a016072\u201316081). Los Alamitos: IEEE."},{"key":"6_CR205","unstructured":"Cai, H., Zhu, L., & Han, S. (2018). ProxylessNAS: direct neural architecture search on target task and hardware. arXiv preprint arXiv:1812.00332."},{"key":"6_CR206","first-page":"10734","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"B. Wu","year":"2019","unstructured":"Wu, B., Dai, X., Zhang, P., Wang, Y., Sun, F., Wu, Y., Tian, Y., Vajda, P., Jia, Y., & Keutzer, K. (2019). Fbnet: hardware-aware efficient convnet design via differentiable neural architecture search. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a010734\u201310742). Los Alamitos: IEEE."},{"key":"6_CR207","doi-asserted-by":"publisher","first-page":"4704","DOI":"10.1109\/ICPR48806.2021.9412130","volume-title":"2020 25th international conference on pattern recognition (ICPR)","author":"J. G. L\u00f3pez","year":"2021","unstructured":"L\u00f3pez, J. G., Agudo, A., & Moreno-Noguer, F. (2021). E-DNAS: differentiable neural architecture search for embedded systems. In 2020 25th international conference on pattern recognition (ICPR) (pp.\u00a04704\u20134711). Los Alamitos: IEEE."},{"key":"6_CR208","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2022.3208187","author":"X. Luo","year":"2022","unstructured":"Luo, X., Liu, d., Kong, H., Huai, S., Chen, H., & Liu, W. (2022). LightNAS: on lightweight and scalable neural architecture search for embedded platforms. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 1\u201314. https:\/\/doi.org\/10.1109\/TCAD.2022.3208187.","journal-title":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"},{"key":"6_CR209","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2020.100234","volume":"12","author":"T. Cassimon","year":"2020","unstructured":"Cassimon, T., Vanneste, S., Bosmans, S., Mercelis, S., & Hellinckx, P. (2020). Designing resource-constrained neural networks using neural architecture search targeting embedded devices. Internet of Things, 12, 100234.","journal-title":"Internet of Things"},{"key":"6_CR210","first-page":"12965","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"A. Wan","year":"2020","unstructured":"Wan, A., Dai, X., Zhang, P., He, Z., Tian, Y., Xie, S., et al. (2020). Fbnetv2: differentiable neural architecture search for spatial and channel dimensions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a012965\u201312974). Los Alamitos: IEEE."},{"key":"6_CR211","first-page":"784","volume-title":"Proceedings of the European conference on computer vision (ECCV)","author":"Y. He","year":"2018","unstructured":"He, Y., Lin, J., Liu, Z., Wang, H., Li, L.-J., & Han, S. (2018). AMC: automl for model compression and acceleration on mobile devices. In Proceedings of the European conference on computer vision (ECCV) (pp.\u00a0784\u2013800). Berlin: Springer."},{"key":"6_CR212","unstructured":"Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015). Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971."},{"key":"6_CR213","unstructured":"Gupta, M., Aravindan, S., Kalisz, A., Chandrasekhar, V., & Jie, L. (2020). Learning to prune deep neural networks via reinforcement learning. arXiv preprint arXiv:2007.04756."},{"key":"6_CR214","first-page":"25656","volume-title":"International conference on machine learning","author":"S. Yu","year":"2022","unstructured":"Yu, S., Mazaheri, A., & Jannesari, A. (2022). Topology-aware network pruning using multi-stage graph embedding and reinforcement learning. In International conference on machine learning (pp.\u00a025656\u201325667). PMLR."},{"key":"6_CR215","first-page":"2029","volume-title":"Proceedings of the IEEE winter conference on applications of computer vision","author":"Z. Wang","year":"2022","unstructured":"Wang, Z., & Li, C. (2022). Channel pruning via lookahead search guided reinforcement learning. In Proceedings of the IEEE winter conference on applications of computer vision (pp.\u00a02029\u20132040). Los Alamitos: IEEE."},{"key":"6_CR216","first-page":"10674","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"M. Yin","year":"2021","unstructured":"Yin, M., Sui, Y., Liao, S., & Yuan, B. (2021). Towards efficient tensor decomposition-based dnn model compression with optimization framework. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a010674\u201310683). Los Alamitos: IEEE."},{"key":"6_CR217","unstructured":"Rokh, B., Azarpeyvand, A., & Khanteymoori, A. (2022). A comprehensive survey on model quantization for deep neural networks. arXiv preprint arXiv:2205.07877."},{"key":"6_CR218","first-page":"5008","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"P. Chen","year":"2021","unstructured":"Chen, P., Liu, S., Zhao, H., & Jia, J. (2021). Distilling knowledge via knowledge review. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a05008\u20135017). Los Alamitos: IEEE."},{"issue":"1","key":"6_CR219","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1631\/FITEE.1700789","volume":"19","author":"J. Cheng","year":"2018","unstructured":"Cheng, J., Wang, P., Li, G., Hu, Q., & Lu, H. (2018). Recent advances in efficient computation of deep convolutional neural networks. Frontiers of Information Technology & Electronic Engineering, 19(1), 64\u201377.","journal-title":"Frontiers of Information Technology & Electronic Engineering"},{"key":"6_CR220","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1007\/978-981-19-1520-8_55","volume-title":"Pattern recognition and data analysis with applications","author":"S. A. Bhalgaonkar","year":"2022","unstructured":"Bhalgaonkar, S. A., Munot, M. V., & Anuse, A. D. (2022). Pruning for compression of visual pattern recognition networks: a survey from deep neural networks perspective. In D. Gupta, R.\u00a0S. Goswami, S. Banerjee, et al. (Eds.), Pattern recognition and data analysis with applications (pp.\u00a0675\u2013687). Berlin: Springer."},{"issue":"1","key":"6_CR221","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1109\/MSP.2017.2765695","volume":"35","author":"Y. Cheng","year":"2018","unstructured":"Cheng, Y., Wang, D., Zhou, P., & Zhang, T. (2018). Model compression and acceleration for deep neural networks: the principles, progress, and challenges. IEEE Signal Processing Magazine, 35(1), 126\u2013136.","journal-title":"IEEE Signal Processing Magazine"},{"key":"6_CR222","doi-asserted-by":"publisher","DOI":"10.1109\/MCE.2022.3181759","author":"C.-H. Wang","year":"2022","unstructured":"Wang, C.-H., Huang, K.-Y., Yao, Y., Chen, J.-C., Shuai, H.-H., & Cheng, W.-H. (2022). Lightweight deep learning: an overview. IEEE Consumer Electronics Magazine, 1\u201312. https:\/\/doi.org\/10.1109\/MCE.2022.3181759.","journal-title":"IEEE Consumer Electronics Magazine"},{"issue":"2","key":"6_CR223","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1109\/TEVC.2018.2791283","volume":"23","author":"Y. Sun","year":"2018","unstructured":"Sun, Y., Yen, G. G., & Yi, Z. (2018). IGD indicator-based evolutionary algorithm for many-objective optimization problems. IEEE Transactions on Evolutionary Computation, 23(2), 173\u2013187.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"3","key":"6_CR224","doi-asserted-by":"publisher","first-page":"1767","DOI":"10.1007\/s10462-019-09719-2","volume":"53","author":"A. Darwish","year":"2020","unstructured":"Darwish, A., Hassanien, A. E., & Das, S. (2020). A survey of swarm and evolutionary computing approaches for deep learning. Artificial Intelligence Review, 53(3), 1767\u20131812.","journal-title":"Artificial Intelligence Review"},{"issue":"4","key":"6_CR225","doi-asserted-by":"publisher","first-page":"2066","DOI":"10.1152\/jn.00200.2018","volume":"120","author":"A. Stamenkovic","year":"2018","unstructured":"Stamenkovic, A., Stapley, P. J., Robins, R., & Hollands, M. A. (2018). Do postural constraints affect eye, head, and arm coordination? Journal of Neurophysiology, 120(4), 2066\u20132082.","journal-title":"Journal of Neurophysiology"},{"key":"6_CR226","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-17476-1","volume-title":"The evolution of the eye","author":"G. Glaeser","year":"2015","unstructured":"Glaeser, G., & Paulus, H. F. (2015). The evolution of the eye. Springer."},{"key":"6_CR227","volume-title":"The hand-eye-brain system of intelligent robot: from interdisciplinary perspective of information science and neuroscience","author":"H. Qiao","year":"2021","unstructured":"Qiao, H., Ma, C., & Li, R. (2021). The hand-eye-brain system of intelligent robot: from interdisciplinary perspective of information science and neuroscience. Berlin: Springer."},{"issue":"10","key":"6_CR228","doi-asserted-by":"publisher","first-page":"11267","DOI":"10.1109\/TCYB.2021.3071312","volume":"52","author":"H. Qiao","year":"2022","unstructured":"Qiao, H., Chen, J., & Huang, X. (2022). A survey of brain-inspired intelligent robots: integration of vision, decision, motion control, and musculoskeletal systems. IEEE Transactions on Cybernetics, 52(10), 11267\u201311280.","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"4","key":"6_CR229","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/TCDS.2018.2843563","volume":"10","author":"X. Huang","year":"2018","unstructured":"Huang, X., Wu, W., Qiao, H., & Ji, Y. (2018). Brain-inspired motion learning in recurrent neural network with emotion modulation. IEEE Transactions on Cognitive and Developmental Systems, 10(4), 1153\u20131164.","journal-title":"IEEE Transactions on Cognitive and Developmental Systems"},{"issue":"6","key":"6_CR230","doi-asserted-by":"publisher","first-page":"2718","DOI":"10.1109\/TMECH.2019.2945135","volume":"24","author":"R. Li","year":"2019","unstructured":"Li, R., & Qiao, H. (2019). A survey of methods and strategies for high-precision robotic grasping and assembly tasks\u2014some new trends. IEEE\/ASME Transactions on Mechatronics, 24(6), 2718\u20132732.","journal-title":"IEEE\/ASME Transactions on Mechatronics"},{"key":"6_CR231","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1109\/ICISCE50968.2020.00216","volume-title":"2020 7th international conference on information science and control engineering (ICISCE)","author":"Z. Chen","year":"2020","unstructured":"Chen, Z., & Qiao, H. (2020). Realizing compliant insertion task based on attractive-region-in-environment. In 2020 7th international conference on information science and control engineering (ICISCE) (pp.\u00a01063\u20131067). Los Alamitos: IEEE."},{"key":"6_CR232","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/978-981-16-3575-5_5","volume-title":"The \u201chand-eye-brain\u201d system of intelligent robot","author":"H. Qiao","year":"2022","unstructured":"Qiao, H., Ma, C., & Li, R. (2022). The concept of \u201cattractive region in environment (ARIE)\u201d and its application in high-precision tasks with low-precision systems. In The \u201chand-eye-brain\u201d system of intelligent robot (pp.\u00a015\u201338). Berlin: Springer."},{"issue":"10","key":"6_CR233","doi-asserted-by":"publisher","first-page":"2335","DOI":"10.1109\/TCYB.2015.2476706","volume":"46","author":"H. Qiao","year":"2015","unstructured":"Qiao, H., Li, Y., Li, F., Xi, X., & Wu, W. (2015). Biologically inspired model for visual cognition achieving unsupervised episodic and semantic feature learning. IEEE Transactions on Cybernetics, 46(10), 2335\u20132347.","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"2","key":"6_CR234","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1109\/TCDS.2017.2749978","volume":"10","author":"P. Yin","year":"2017","unstructured":"Yin, P., Qiao, H., Wu, W., Qi, L., Li, Y., Zhong, S., & Zhang, B. (2017). A novel biologically inspired visual cognition model: automatic extraction of semantics, formation of integrated concepts, and reselection features for ambiguity. IEEE Transactions on Cognitive and Developmental Systems, 10(2), 420\u2013431.","journal-title":"IEEE Transactions on Cognitive and Developmental Systems"},{"key":"6_CR235","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/978-981-16-3575-5_12","volume-title":"The \u201chand-eye-brain\u201d system of intelligent robot","author":"H. Qiao","year":"2022","unstructured":"Qiao, H., Ma, C., & Li, R. (2022). Biologically inspired visual model with preliminary cognition and active attention adjustment. In The \u201chand-eye-brain\u201d system of intelligent robot (pp.\u00a0131\u2013150). Berlin: Springer."},{"issue":"8","key":"6_CR236","doi-asserted-by":"publisher","first-page":"4624","DOI":"10.1109\/TSMC.2019.2933152","volume":"51","author":"X. Huang","year":"2019","unstructured":"Huang, X., Wu, W., & Qiao, H. (2019). Connecting model-based and model-free control with emotion modulation in learning systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(8), 4624\u20134638.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"issue":"1","key":"6_CR237","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/TCDS.2019.2963545","volume":"13","author":"X. Huang","year":"2020","unstructured":"Huang, X., Wu, W., & Qiao, H. (2020). Computational modeling of emotion-motivated decisions for continuous control of mobile robots. IEEE Transactions on Cognitive and Developmental Systems, 13(1), 31\u201344.","journal-title":"IEEE Transactions on Cognitive and Developmental Systems"},{"issue":"1","key":"6_CR238","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1109\/TEC.2020.3006098","volume":"36","author":"W. Yu","year":"2020","unstructured":"Yu, W., Hua, W., Qi, J., Zhang, H., Zhang, G., Xiao, H., Xu, S., & Ma, G. (2020). Coupled magnetic field-thermal network analysis of modular-spoke-type permanent-magnet machine for electric motorcycle. IEEE Transactions on Energy Conversion, 36(1), 120\u2013130.","journal-title":"IEEE Transactions on Energy Conversion"},{"issue":"6","key":"6_CR239","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A. Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84\u201390.","journal-title":"Communications of the ACM"},{"issue":"10","key":"6_CR240","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1007\/s12541-016-0153-2","volume":"17","author":"S. A. Nahian","year":"2016","unstructured":"Nahian, S. A., Truong, D. Q., Chowdhury, P., Das, D., & Ahn, K. K. (2016). Modeling and fault tolerant control of an electro-hydraulic actuator. International Journal of Precision Engineering and Manufacturing, 17(10), 1285\u20131297.","journal-title":"International Journal of Precision Engineering and Manufacturing"},{"issue":"4","key":"6_CR241","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1109\/TMECH.2010.2085009","volume":"16","author":"H. Wei","year":"2010","unstructured":"Wei, H., Chen, Y., Tan, J., & Wang, T. (2010). Sambot: a self-assembly modular robot system. IEEE\/ASME Transactions on Mechatronics, 16(4), 745\u2013757.","journal-title":"IEEE\/ASME Transactions on Mechatronics"},{"issue":"3","key":"6_CR242","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1109\/MRA.2010.937859","volume":"17","author":"K. Gilpin","year":"2010","unstructured":"Gilpin, K., & Rus, D. (2010). Modular robot systems. IEEE Robotics & Automation Magazine, 17(3), 38\u201355.","journal-title":"IEEE Robotics & Automation Magazine"},{"key":"6_CR243","first-page":"1495","volume-title":"12th IEEE international conference on fuzzy systems","author":"T. Fukuda","year":"2003","unstructured":"Fukuda, T., & Kubota, N. (2003). Computational intelligence for robotic systems. In 12th IEEE international conference on fuzzy systems (pp.\u00a01495\u20131508). Los Alamitos: IEEE."},{"issue":"13","key":"6_CR244","doi-asserted-by":"publisher","DOI":"10.3390\/s22134950","volume":"22","author":"A. Gallala","year":"2022","unstructured":"Gallala, A., Kumar, A. A., Hichri, B., & Plapper, P. (2022). Digital twin for human\u2013robot interactions by means of industry 4.0 enabling technologies. Sensors, 22(13), 4950.","journal-title":"Sensors"},{"issue":"2","key":"6_CR245","doi-asserted-by":"publisher","first-page":"15","DOI":"10.2308\/jeta-52311","volume":"15","author":"S. G. Sutton","year":"2018","unstructured":"Sutton, S. G., Arnold, V., & Holt, M. (2018). How much automation is too much? Keeping the human relevant in knowledge work. Journal of Emerging Technologies in Accounting, 15(2), 15\u201325.","journal-title":"Journal of Emerging Technologies in Accounting"},{"issue":"49","key":"6_CR246","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abe4385","volume":"5","author":"M. Dorigo","year":"2020","unstructured":"Dorigo, M., Theraulaz, G., & Trianni, V. (2020). Reflections on the future of swarm robotics. Science Robotics, 5(49), eabe4385.","journal-title":"Science Robotics"},{"key":"6_CR247","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106342","volume":"93","author":"A. Rodr\u00edguez-Molina","year":"2020","unstructured":"Rodr\u00edguez-Molina, A., Mezura-Montes, E., Villarreal-Cervantes, M. G., & Aldape-P\u00e9rez, M. (2020). Multi-objective meta-heuristic optimization in intelligent control: a survey on the controller tuning problem. Applied Soft Computing, 93, 106342.","journal-title":"Applied Soft Computing"},{"key":"6_CR248","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.neucom.2018.05.083","volume":"312","author":"M. Wang","year":"2018","unstructured":"Wang, M., & Deng, W. (2018). Deep visual domain adaptation: a survey. Neurocomputing, 312, 135\u2013153.","journal-title":"Neurocomputing"},{"key":"6_CR249","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3178128","author":"J. Wang","year":"2022","unstructured":"Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., & Yu, P. (2022). Generalizing to unseen domains: a survey on domain generalization. IEEE Transactions on Knowledge and Data Engineering, 1\u201314. https:\/\/doi.org\/10.1109\/TKDE.2022.3178128.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"6_CR250","first-page":"8320","volume-title":"Proceedings of the IEEE international conference on computer vision","author":"H. Bai","year":"2021","unstructured":"Bai, H., Zhou, F., Hong, L., Ye, N., Chan, S. G., & Li, Z. (2021). NAS-OOD: neural architecture search for out-of-distribution generalization. In Proceedings of the IEEE international conference on computer vision (pp.\u00a08320\u20138329). Los Alamitos: IEEE."},{"issue":"5","key":"6_CR251","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1109\/TEVC.2021.3097937","volume":"25","author":"Y.-W. Wen","year":"2021","unstructured":"Wen, Y.-W., Peng, S.-H., & Ting, C.-K. (2021). Two-stage evolutionary neural architecture search for transfer learning. IEEE Transactions on Evolutionary Computation, 25(5), 928\u2013940.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"6_CR252","first-page":"789","volume-title":"Advances in neural information processing systems 33","author":"Y. Li","year":"2020","unstructured":"Li, Y., Yang, Z., Wang, Y., & Xu, C. (2020). Adapting neural architectures between domains. In H. Larochelle, M. Ranzato, R. Hadsell, M.\u00a0F. Balcan, & H. Lin (Eds.), Advances in neural information processing systems 33 (pp.\u00a0789\u2013798). Red Hook: Curran Associates."},{"issue":"10","key":"6_CR253","doi-asserted-by":"publisher","first-page":"6610","DOI":"10.1109\/TII.2021.3129813","volume":"18","author":"N. Guo","year":"2022","unstructured":"Guo, N., Gu, K., Qiao, J., & Liu, H. (2022). Active vision for deep visual learning: a unified pooling framework. IEEE Transactions on Industrial Informatics, 18(10), 6610\u20136618. https:\/\/doi.org\/10.1109\/TII.2021.3129813.","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"1","key":"6_CR254","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-09405-4","volume":"12","author":"J. Ito","year":"2022","unstructured":"Ito, J., Joana, C., Yamane, Y., Fujita, I., Tamura, H., Maldonado, P. E., & Gr\u00fcn, S. (2022). Latency shortening with enhanced sparseness and responsiveness in V1 during active visual sensing. Scientific Reports, 12(1), 1\u201317.","journal-title":"Scientific Reports"},{"issue":"9","key":"6_CR255","doi-asserted-by":"publisher","first-page":"2953","DOI":"10.1109\/TPAMI.2021.3059510","volume":"43","author":"Y. Xu","year":"2021","unstructured":"Xu, Y., Xie, L., Dai, W., Zhang, X., Chen, X., Qi, G.-J., Xiong, H., & Tian, Q. (2021). Partially-connected neural architecture search for reduced computational redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 2953\u20132970.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"5","key":"6_CR256","doi-asserted-by":"publisher","first-page":"1230","DOI":"10.1109\/TRO.2016.2596772","volume":"32","author":"X. Lan","year":"2016","unstructured":"Lan, X., & Schwager, m. (2016). Rapidly exploring random cycles: persistent estimation of spatiotemporal fields with multiple sensing robots. IEEE Transactions on Robotics, 32(5), 1230\u20131244.","journal-title":"IEEE Transactions on Robotics"},{"key":"6_CR257","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1109\/ICRA.2015.7139224","volume-title":"2015 IEEE international conference on robotics and automation (ICRA)","author":"H. Carrillo","year":"2015","unstructured":"Carrillo, H., Dames, P., Kumar, V., & Castellanos, J. A. (2015). Autonomous robotic exploration using occupancy grid maps and graph slam based on Shannon and R\u00e9nyi entropy. In 2015 IEEE international conference on robotics and automation (ICRA) (pp.\u00a0487\u2013494). Los Alamitos: IEEE."},{"key":"6_CR258","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1016\/j.ast.2018.12.030","volume":"85","author":"Y. Meng","year":"2019","unstructured":"Meng, Y., Wang, W., Han, H., & Ban, J. (2019). A visual\/inertial integrated landing guidance method for UAV landing on the ship. Aerospace Science and Technology, 85, 474\u2013480.","journal-title":"Aerospace Science and Technology"},{"issue":"7","key":"6_CR259","doi-asserted-by":"publisher","first-page":"5059","DOI":"10.1109\/TII.2020.3015730","volume":"17","author":"J. Zheng","year":"2020","unstructured":"Zheng, J., Yang, T., Liu, H., Su, T., & Wan, L. (2020). Accurate detection and localization of unmanned aerial vehicle swarms-enabled mobile edge computing system. IEEE Transactions on Industrial Informatics, 17(7), 5059\u20135067.","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"20","key":"6_CR260","doi-asserted-by":"publisher","first-page":"15372","DOI":"10.1109\/JIOT.2021.3064376","volume":"8","author":"J. Zheng","year":"2021","unstructured":"Zheng, J., Chen, R., Yang, T., Liu, X., Liu, H., Su, T., & Wan, L. (2021). An efficient strategy for accurate detection and localization of UAV swarms. IEEE Internet of Things Journal, 8(20), 15372\u201315381.","journal-title":"IEEE Internet of Things Journal"},{"key":"6_CR261","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.neunet.2019.01.012","volume":"113","author":"G. I. Parisi","year":"2019","unstructured":"Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019). Continual lifelong learning with neural networks: a review. Neural Networks, 113, 54\u201371.","journal-title":"Neural Networks"},{"key":"6_CR262","first-page":"1","volume-title":"2021 international joint conference on neural networks (IJCNN)","author":"X. Du","year":"2021","unstructured":"Du, X., Li, Z., Sun, J., Liu, F., & Cao, Y. (2021). Evolutionary NAS in light of model stability for accurate continual learning. In 2021 international joint conference on neural networks (IJCNN) (pp.\u00a01\u20138). Los Alamitos: IEEE."},{"issue":"2","key":"6_CR263","first-page":"690","volume":"34","author":"Q. Gao","year":"2022","unstructured":"Gao, Q., Luo, Z., Klabjan, D., & Zhang, F. (2022). Efficient architecture search for continual learning. IEEE Transactions on Neural Networks and Learning Systems, 34(2), 690\u2013702.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"6_CR264","first-page":"3523","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"M. Mundt","year":"2021","unstructured":"Mundt, M., Pliushch, I., & Ramesh, V. (2021). Neural architecture search of deep priors: towards continual learning without catastrophic interference. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a03523\u20133532). Los Alamitos: IEEE."}],"container-title":["Visual Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44267-023-00006-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44267-023-00006-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44267-023-00006-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T02:06:47Z","timestamp":1683511607000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44267-023-00006-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,8]]},"references-count":264,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["6"],"URL":"https:\/\/doi.org\/10.1007\/s44267-023-00006-x","relation":{},"ISSN":["2731-9008"],"issn-type":[{"value":"2731-9008","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,8]]},"assertion":[{"value":"1 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"2"}}