{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:43:25Z","timestamp":1777657405491,"version":"3.51.4"},"reference-count":92,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:00:00Z","timestamp":1743120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Climate Change, Energy, the Environment and Water of the Australian Federal Government","award":["ICIRN000077"],"award-info":[{"award-number":["ICIRN000077"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>The rapid adoption of artificial intelligence (AI) systems, such as predictive AI, generative AI, and explainable AI, is in contrast to the slower development and uptake of robotic AI systems. Dynamic environments, sensory processing, mechanical movements, power management, and safety are inherent complexities of robotic intelligence capabilities that can be addressed using novel AI approaches. The current AI landscape is dominated by machine learning techniques, specifically deep learning algorithms, that have been effective in addressing some of these challenges. However, these algorithms are subject to computationally complex processing and operational needs such as high data dependency. In this paper, we propose a computation-efficient and data-efficient framework for robotic motion intelligence (RMI) based on vector symbolic architectures (VSAs) and blockchain-based smart contracts. The capabilities of VSAs are leveraged for computationally efficient learning and noise suppression during perception, motion, movement, and decision-making tasks. As a distributed ledger technology, smart contracts address data dependency through a decentralized, distributed, and secure transactions ledger that satisfies contractual conditions. An empirical evaluation of the framework confirms its value and contribution towards addressing the practical challenges of robotic motion intelligence by significantly reducing the learnable parameters by 10 times while preserving sufficient accuracy compared to existing deep learning solutions.<\/jats:p>","DOI":"10.3390\/robotics14040038","type":"journal-article","created":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T13:36:49Z","timestamp":1743169009000},"page":"38","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Robotic Motion Intelligence Using Vector Symbolic Architectures and Blockchain-Based Smart Contracts"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3878-5969","authenticated-orcid":false,"given":"Daswin","family":"De Silva","sequence":"first","affiliation":[{"name":"Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia"}]},{"given":"Sudheera","family":"Withanage","sequence":"additional","affiliation":[{"name":"Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia"}]},{"given":"Vidura","family":"Sumanasena","sequence":"additional","affiliation":[{"name":"Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7670-4495","authenticated-orcid":false,"given":"Lakshitha","family":"Gunasekara","sequence":"additional","affiliation":[{"name":"Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6212-8312","authenticated-orcid":false,"given":"Harsha","family":"Moraliyage","sequence":"additional","affiliation":[{"name":"Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2157-3767","authenticated-orcid":false,"given":"Nishan","family":"Mills","sequence":"additional","affiliation":[{"name":"Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia"}]},{"given":"Milos","family":"Manic","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"312","DOI":"10.3109\/10929080109146301","article-title":"State of the art in surgical robotics: Clinical applications and technology challenges","volume":"6","author":"Cleary","year":"2001","journal-title":"Comput. Aided Surg."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1109\/MRA.2016.2582718","article-title":"Stiffening in soft robotics: A review of the state of the art","volume":"23","author":"Manti","year":"2016","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_3","unstructured":"Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Chen, X., Choromanski, K., Ding, T., Driess, D., Dubey, A., and Finn, C. (2023). Rt-2: Vision-language-action models transfer web knowledge to robotic control. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Dabis, J., Finn, C., Gopalakrishnan, K., Hausman, K., Herzog, A., and Hsu, J. (2022). Rt-1: Robotics transformer for real-world control at scale. arXiv.","DOI":"10.15607\/RSS.2023.XIX.025"},{"key":"ref_5","unstructured":"Reed, S., Zolna, K., Parisotto, E., Colmenarejo, S.G., Novikov, A., Barth-Maron, G., Gimenez, M., Sulsky, Y., Kay, J., and Springenberg, J.T. (2022). A generalist agent. arXiv."},{"key":"ref_6","unstructured":"O\u2019Neill, A., Rehman, A., Gupta, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., and Mandlekar, A. (2023). Open x-embodiment: Robotic learning datasets and rt-x models. arXiv."},{"key":"ref_7","unstructured":"Pomerleau, D.A. (1988). Alvinn: An autonomous land vehicle in a neural network. Adv. Neural Inf. Process. Syst., 1."},{"key":"ref_8","unstructured":"Pomerleau, D.A. (2012). Neural Network Perception for Mobile Robot Guidance, Springer Science & Business Media."},{"key":"ref_9","unstructured":"Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L.D., Monfort, M., Muller, U., and Zhang, J. (2016). End to end learning for self-driving cars. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Cultrera, L., Seidenari, L., Becattini, F., Pala, P., and Del Bimbo, A. (2020, January 14\u201319). Explaining autonomous driving by learning end-to-end visual attention. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00178"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1038\/s42256-024-00917-4","article-title":"General-purpose foundation models for increased autonomy in robot-assisted surgery","volume":"6","author":"Schmidgall","year":"2024","journal-title":"Nat. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, A., Islam, M., Xu, M., Zhang, Y., and Ren, H. (2023, January 8\u201312). Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Vancouver, BC, Canada.","DOI":"10.1007\/978-3-031-47401-9_23"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.Y. (2023, January 2\u20133). Segment anything. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Adikari, A., De Silva, D., Ranasinghe, W.K., Bandaragoda, T., Alahakoon, O., Persad, R., Lawrentschuk, N., Alahakoon, D., and Bolton, D. (2020). Can online support groups address psychological morbidity of cancer patients? An artificial intelligence based investigation of prostate cancer trajectories. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0229361"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2535","DOI":"10.1109\/JBHI.2020.2990529","article-title":"Homecare robotic systems for healthcare 4.0: Visions and enabling technologies","volume":"24","author":"Yang","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_16","unstructured":"De Silva, D., Burstein, F., Jelinek, H.F., and Stranieri, A. (2015). Addressing the complexities of big data analytics in healthcare: The diabetes screening case. Australas. J. Inf. Syst., 19, Available online: https:\/\/proceedings.neurips.cc\/paper\/1988\/hash\/812b4ba287f5ee0bc9d43bbf5bbe87fb-Abstract.html."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Rao, Q., and Frtunikj, J. (2018, January 28). Deep learning for self-driving cars: Chances and challenges. Proceedings of the 1st International Workshop on Software Engineering for AI in Autonomous Systems, Gothenburg Sweden.","DOI":"10.1145\/3194085.3194087"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Nallaperuma, D., De Silva, D., Alahakoon, D., and Yu, X. (2018, January 21\u201323). Intelligent detection of driver behavior changes for effective coordination between autonomous and human driven vehicles. Proceedings of the IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, USA.","DOI":"10.1109\/IECON.2018.8591357"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/MCOMSTD.0001.2000080","article-title":"Accelerating power grid monitoring with flying robots and artificial intelligence","volume":"5","author":"Chehri","year":"2021","journal-title":"IEEE Commun. Stand. Mag."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"De Silva, D., Yu, X., Alahakoon, D., and Holmes, G. (2011, January 20\u201323). Semi-supervised classification of characterized patterns for demand forecasting using smart electricity meters. Proceedings of the 2011 International Conference on Electrical Machines and Systems, Beijing, China.","DOI":"10.1109\/ICEMS.2011.6073434"},{"key":"ref_21","unstructured":"Taheri, H., Hosseini, S.R., and Nekoui, M.A. (2024). Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"de Moraes, L.D., Kich, V.A., Kolling, A.H., Bottega, J.A., Grando, R.B., Cukla, A.R., and Gamarra, D.F.T. (2023). Enhanced Low-Dimensional Sensing Mapless Navigation of Terrestrial Mobile Robots Using Double Deep Reinforcement Learning Techniques. arXiv.","DOI":"10.1109\/LARS\/SBR\/WRE59448.2023.10333028"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"11781","DOI":"10.1109\/JSEN.2020.3003121","article-title":"Multi-Modal Sensor Fusion-Based Deep Neural Network for End-to-End Autonomous Driving with Scene Understanding","volume":"21","author":"Huang","year":"2021","journal-title":"IEEE Sensors J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/MRA.2010.936952","article-title":"Imitation and reinforcement learning","volume":"17","author":"Kober","year":"2010","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"13753","DOI":"10.1109\/ACCESS.2022.3146518","article-title":"A survey of domain-specific architectures for reinforcement learning","volume":"10","author":"Rothmann","year":"2022","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"27091","DOI":"10.1109\/ACCESS.2017.2777827","article-title":"System design perspective for human-level agents using deep reinforcement learning: A survey","volume":"5","author":"Nguyen","year":"2017","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"14128","DOI":"10.1109\/TITS.2022.3144867","article-title":"A survey on imitation learning techniques for end-to-end autonomous vehicles","volume":"23","author":"Yi","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Haavaldsen, H., Aasboe, M., and Lindseth, F. (2019, January 27\u201328). Autonomous vehicle control: End-to-end learning in simulated urban environments. Proceedings of the Nordic Artificial Intelligence Research and Development: Third Symposium of the Norwegian AI Society, NAIS 2019, Trondheim, Norway. Proceedings 3.","DOI":"10.1007\/978-3-030-35664-4_4"},{"key":"ref_29","unstructured":"Codevilla, F., Santana, E., L\u00f3pez, A.M., and Gaidon, A. (November, January 27). Exploring the limitations of behavior cloning for autonomous driving. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Russell, S. (1998, January 24\u201326). Learning agents for uncertain environments. Proceedings of the Eleventh Annual Conference on Computational Learning Theory, Madison, WI, USA.","DOI":"10.1145\/279943.279964"},{"key":"ref_31","unstructured":"Ng, A.Y., and Russell, S. (July, January 29). Algorithms for inverse reinforcement learning. Proceedings of the Icml, Stanford, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Abbeel, P., and Ng, A.Y. (2004, January 4\u20138). Apprenticeship learning via inverse reinforcement learning. Proceedings of the Twenty-First International Conference on Machine Learning, Banff, AB, Canada.","DOI":"10.1145\/1015330.1015430"},{"key":"ref_33","unstructured":"Sadigh, D., Sastry, S., Seshia, S.A., and Dragan, A.D. (2016, January 18\u201322). Planning for autonomous cars that leverage effects on human actions. Proceedings of the Robotics: Science and Systems, Ann Arbor, MI, USA."},{"key":"ref_34","unstructured":"Ziebart, B.D., Maas, A., Bagnell, J.A., and Dey, A.K. (2008, January 13\u201317). Maximum Entropy Inverse Reinforcement Learning. Proceedings of the Proc. AAAI, Chicago, IL, USA."},{"key":"ref_35","unstructured":"Wulfmeier, M., Ondruska, P., and Posner, I. (2015). Maximum entropy deep inverse reinforcement learning. arXiv."},{"key":"ref_36","unstructured":"Ho, J., and Ermon, S. (2016). Generative adversarial imitation learning. Adv. Neural Inf. Process. Syst., 29."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yang, C., Liang, P., Ajoudani, A., Li, Z., and Bicchi, A. (2016, January 9\u201314). Development of a robotic teaching interface for human to human skill transfer. Proceedings of the 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Republic of Korea.","DOI":"10.1109\/IROS.2016.7759130"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Luo, J., Dong, X., and Yang, H. (2015, January 27\u201330). Session search by direct policy learning. Proceedings of the 2015 International Conference on the Theory of Information Retrieval, Northampton, MA, USA.","DOI":"10.1145\/2808194.2809461"},{"key":"ref_39","unstructured":"Ross, S., Gordon, G., and Bagnell, D. (2011, January 11\u201313). A reduction of imitation learning and structured prediction to no-regret online learning. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings, Fort Lauderdale, FL, USA."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Cho, K. (2016). Query-efficient imitation learning for end-to-end autonomous driving. arXiv.","DOI":"10.1609\/aaai.v31i1.10857"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, G., Mueller, M., Casser, V., Smith, N., Michels, D.L., and Ghanem, B. (2018). Oil: Observational imitation learning. arXiv.","DOI":"10.15607\/RSS.2019.XV.005"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"902","DOI":"10.1007\/s11263-018-1073-7","article-title":"Sim4cv: A photo-realistic simulator for computer vision applications","volume":"126","author":"Casser","year":"2018","journal-title":"Int. J. Comput. Vis."},{"key":"ref_43","unstructured":"Ruiz-del Solar, J., Loncomilla, P., and Soto, N. (2018). A survey on deep learning methods for robot vision. arXiv."},{"key":"ref_44","unstructured":"Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., and Oliva, A. (2014). Learning deep features for scene recognition using places database. Adv. Neural Inf. Process. Syst., 27."},{"key":"ref_45","unstructured":"Gomez-Ojeda, R., Lopez-Antequera, M., Petkov, N., and Gonzalez-Jimenez, J. (2015). Training a convolutional neural network for appearance-invariant place recognition. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"S\u00fcnderhauf, N., Dayoub, F., McMahon, S., Talbot, B., Schulz, R., Corke, P., Wyeth, G., Upcroft, B., and Milford, M. (2016, January 16\u201321). Place categorization and semantic mapping on a mobile robot. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487796"},{"key":"ref_47","unstructured":"Liao, Y., Kodagoda, S., Wang, Y., Shi, L., and Liu, Y. (2016, January 16\u201321). Understand scene categories by objects: A semantic regularized scene classifier using convolutional neural networks. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1109\/ETFA46521.2020.9212098","article-title":"A deep learning approach for work related stress detection from audio streams in cyber physical environments","volume":"Volume 1","author":"Madhavi","year":"2020","journal-title":"Proceedings of the 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"051002","DOI":"10.1115\/1.4056573","article-title":"Multi-objective evolutionary algorithm with machine learning and local search for an energy-efficient disassembly line balancing problem in remanufacturing","volume":"145","author":"Tian","year":"2023","journal-title":"J. Manuf. Sci. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.jmsy.2024.03.014","article-title":"A chance-constraint programming approach for a disassembly line balancing problem under uncertainty","volume":"74","author":"Zhang","year":"2024","journal-title":"J. Manuf. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chitta, K., Prakash, A., and Geiger, A. (2021). NEAT: Neural Attention Fields for End-to-End Autonomous Driving. arXiv.","DOI":"10.1109\/ICCV48922.2021.01550"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Chen, D., and Kr\u00e4henb\u00fchl, P. (2022). Learning from All Vehicles. arXiv.","DOI":"10.1109\/CVPR52688.2022.01671"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Hu, Y., Yang, J., Chen, L., Li, K., Sima, C., Zhu, X., Chai, S., Du, S., Lin, T., and Wang, W. (2023). Planning-oriented Autonomous Driving. arXiv.","DOI":"10.1109\/CVPR52729.2023.01712"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, W., Li, H., Xie, E., Sima, C., Lu, T., Yu, Q., and Dai, J. (2022). BEVFormer: Learning Bird\u2019s-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers. arXiv.","DOI":"10.1007\/978-3-031-20077-9_1"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhang, T., Chen, X., Wang, Y., Wang, Y., and Zhao, H. (2022). MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries. arXiv.","DOI":"10.1109\/CVPRW56347.2022.00500"},{"key":"ref_56","unstructured":"Gayler, R.W. (2004). Vector symbolic architectures answer Jackendoff\u2019s challenges for cognitive neuroscience. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4523","DOI":"10.1007\/s10462-021-10110-3","article-title":"A comparison of vector symbolic architectures","volume":"55","author":"Schlegel","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s12559-009-9009-8","article-title":"Hyperdimensional computing: An introduction to computing in distributed representation with high-dimensional random vectors","volume":"1","author":"Kanerva","year":"2009","journal-title":"Cogn. Comput."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1038\/s41928-020-0410-3","article-title":"In-memory hyperdimensional computing","volume":"3","author":"Karunaratne","year":"2020","journal-title":"Nat. Electron."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1038\/s41928-020-00510-8","article-title":"A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition","volume":"4","author":"Moin","year":"2021","journal-title":"Nat. Electron."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Schlegel, K., Rachkovskij, D.A., Osipov, E., Protzel, P., and Neubert, P. (July, January 30). Learnable Weighted Superposition in HDC and its Application to Multi-channel Time Series Classification. Proceedings of the 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan.","DOI":"10.1109\/IJCNN60899.2024.10650604"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"6583","DOI":"10.1109\/TNNLS.2022.3211274","article-title":"Hyperseed: Unsupervised learning with vector symbolic architectures","volume":"35","author":"Osipov","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Kleyko, D., Osipov, E., De Silva, D., Wiklund, U., and Alahakoon, D. (2019, January 14\u201319). Integer self-organizing maps for digital hardware. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8852471"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Hersche, M., Karunaratne, G., Cherubini, G., Benini, L., Sebastian, A., and Rahimi, A. (2022, January 18\u201324). Constrained few-shot class-incremental learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00885"},{"key":"ref_65","first-page":"404","article-title":"The transformative potential of vector symbolic architecture for cognitive processing at the network edge","volume":"Volume 13206","author":"Bent","year":"2024","journal-title":"Proceedings of the Artificial Intelligence for Security and Defence Applications II"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Fung, M.L., Chen, M.Z., and Chen, Y.H. (2017, January 28\u201330). Sensor fusion: A review of methods and applications. Proceedings of the 2017 29th Chinese Control and Decision Conference (CCDC), Chongqing, China.","DOI":"10.1109\/CCDC.2017.7979175"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Chandrasekaran, B., Gangadhar, S., and Conrad, J.M. (April, January 30). A survey of multisensor fusion techniques, architectures and methodologies. Proceedings of the SoutheastCon 2017, Concord, NC, USA.","DOI":"10.1109\/SECON.2017.7925311"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Xie, S., Dai, H., Chen, X., and Wang, H. (2017, January 25\u201330). An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends. Proceedings of the 2017 IEEE International Congress on Big Data (BigData Congress), Honolulu, HI, USA.","DOI":"10.1109\/BigDataCongress.2017.85"},{"key":"ref_69","unstructured":"Szabo, N. (2024, December 31). Smart Contracts. Available online: https:\/\/www.fon.hum.uva.nl\/rob\/Courses\/InformationInSpeech\/CDROM\/Literature\/LOTwinterschool2006\/szabo.best.vwh.net\/smart.contracts.html."},{"key":"ref_70","unstructured":"Novak, I. (2023). A Systematic Analysis of Cryptocurrencies. [Ph.D. Thesis, Faculty of Economics and Business, University of Zagreb]."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"eabm4636","DOI":"10.1126\/scirobotics.abm4636","article-title":"Robot Swarms Neutralize Harmful Byzantine Robots Using a Blockchain-Based Token Economy","volume":"8","author":"Strobel","year":"2023","journal-title":"Sci. Robot."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1109\/TRO.2021.3104243","article-title":"Following Leaders in Byzantine Multirobot Systems by Using Blockchain Technology","volume":"38","author":"Ferrer","year":"2022","journal-title":"IEEE Trans. Robot."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Wang, T. (2024). PrivShieldROS: An Extended Robot Operating System Integrating Ethereum and Interplanetary File System for Enhanced Sensor Data Privacy. Sensors, 24.","DOI":"10.3390\/s24103241"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Salimi, S., Mor\u00f3n, P.T., Queralta, J.P., and Westerlund, T. (November, January 26). Secure Heterogeneous Multi-Robot Collaboration and Docking with Hyperledger Fabric Blockchain. Proceedings of the 2022 IEEE 8th World Forum on Internet of Things (WF-IoT), Yokohama, Japan.","DOI":"10.1109\/WF-IoT54382.2022.10152244"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/MCOM.2018.1701095","article-title":"When Mobile Blockchain Meets Edge Computing","volume":"56","author":"Xiong","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_76","first-page":"3988070","article-title":"Smart Payment Contract Mechanism Based on Blockchain Smart Contract Mechanism","volume":"2021","author":"Ge","year":"2021","journal-title":"Sci. Program."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1017\/S0140525X0000546X","article-title":"Against direct perception","volume":"3","author":"Ullman","year":"1980","journal-title":"Behav. Brain Sci."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The kitti dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_79","unstructured":"\u00d6lsner, F., and Milz, S. (2019, January 8\u201314). Catch me, if you can! a mediated perception approach towards fully autonomous drone racing. Proceedings of the NeurIPS 2019 Competition and Demonstration Track, PMLR, Vancouver, BC, Canada."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Chen, C., Seff, A., Kornhauser, A., and Xiao, J. (2015, January 7\u201313). DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.312"},{"key":"ref_81","unstructured":"Yuan, D., Ferm\u00fcller, C., Rabbani, T., Huang, F., and Aloimonos, Y. (2024). A Linear Time and Space Local Point Cloud Geometry Encoder via Vectorized Kernel Mixture (VecKM). arXiv."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Neubert, P., and Schubert, S. (2021, January 20\u201325). Hyperdimensional computing as a framework for systematic aggregation of image descriptors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01666"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Imani, M., Kong, D., Rahimi, A., and Rosing, T. (2017, January 8\u20139). Voicehd: Hyperdimensional computing for efficient speech recognition. Proceedings of the 2017 IEEE International Conference on Rebooting Computing (ICRC), Washington, DC, USA.","DOI":"10.1109\/ICRC.2017.8123650"},{"key":"ref_84","unstructured":"Neubert, P., Schubert, S., and Protzel, P. (2024, December 31). Learning Vector Symbolic Architectures for Reactive Robot Behaviours. Available online: https:\/\/d-nb.info\/1214377416\/34."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Noh, H., Araujo, A., Sim, J., Weyand, T., and Han, B. (2017, January 22\u201329). Large-scale image retrieval with attentive deep local features. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.374"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1109\/72.377968","article-title":"Holographic reduced representations","volume":"6","author":"Plate","year":"1995","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_87","unstructured":"Wu, P., Jia, X., Chen, L., Yan, J., Li, H., and Qiao, Y. (2022). Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline. arXiv."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Hu, S., Chen, L., Wu, P., Li, H., Yan, J., and Tao, D. (2022). ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning. arXiv.","DOI":"10.1007\/978-3-031-19839-7_31"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s11721-012-0072-5","article-title":"ARGoS: A Modular, Parallel, Multi-Engine Simulator for Multi-Robot Systems","volume":"6","author":"Pinciroli","year":"2012","journal-title":"Swarm Intell."},{"key":"ref_90","unstructured":"Pacheco, A., Denis, U., Zakir, R., Strobel, V., Reina, A., and Dorigo, M. (2024). Toychain: A Simple Blockchain for Research in Swarm Robotics. arXiv."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Lopes, V., and Alexandre, L.A. (2018). An overview of blockchain integration with robotics and artificial intelligence. arXiv.","DOI":"10.5195\/ledger.2019.171"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"103245","DOI":"10.1016\/j.jnca.2021.103245","article-title":"A survey on blockchain in robotics: Issues, opportunities, challenges and future directions","volume":"196","author":"Aditya","year":"2021","journal-title":"J. Netw. Comput. Appl."}],"container-title":["Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-6581\/14\/4\/38\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:04:30Z","timestamp":1760029470000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-6581\/14\/4\/38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,28]]},"references-count":92,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["robotics14040038"],"URL":"https:\/\/doi.org\/10.3390\/robotics14040038","relation":{},"ISSN":["2218-6581"],"issn-type":[{"value":"2218-6581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,28]]}}}