{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:52:55Z","timestamp":1743051175141,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":16,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819607884"},{"type":"electronic","value":"9789819607891"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-0789-1_27","type":"book-chapter","created":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T19:26:15Z","timestamp":1737660375000},"page":"365-380","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Enhanced DMP Approach for\u00a0Robotic Manipulator Autonomous Obstacle Avoidance Using Dynamic Potential Function"],"prefix":"10.1007","author":[{"given":"Xinxin","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiming","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaonan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"issue":"2","key":"27_CR1","first-page":"134","volume":"4","author":"F Kamil","year":"2015","unstructured":"Kamil, F., Tang, S., Khaksar, W., et al.: A review on motion planning and obstacle avoidance approaches in dynamic environments. Adv. Robot. Auto. 4(2), 134\u2013142 (2015)","journal-title":"Adv. Robot. Auto."},{"key":"27_CR2","doi-asserted-by":"crossref","unstructured":"Sabe, K., Fukuchi, M., Gutmann, J.S., et al.: Obstacle avoidance and path planning for humanoid robots using stereo vision. In: IEEE International Conference on Robotics and Automation, ICRA 2004, vol. 1, pp. 592\u2013597 (2004)","DOI":"10.1109\/ROBOT.2004.1307213"},{"issue":"2","key":"27_CR3","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1162\/NECO_a_00393","volume":"25","author":"AJ Ijspeert","year":"2013","unstructured":"Ijspeert, A.J., Nakanishi, J., Hoffmann, H., et al.: Dynamical movement primitives: learning attractor models for motor behaviors. Neural Comput. 25(2), 328\u2013373 (2013)","journal-title":"Neural Comput."},{"key":"27_CR4","doi-asserted-by":"crossref","unstructured":"Schaal, S.: Dynamic movement primitives-a framework for motor control in humans and humanoid robotics. In: Adaptive Motion of Animals and Machines, Tokyo, pp. 261\u2013280. Springer Tokyo (2006)","DOI":"10.1007\/4-431-31381-8_23"},{"key":"27_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2021.103935","volume":"148","author":"M Wu","year":"2022","unstructured":"Wu, M., Taetz, B., He, Y., et al.: An adaptive learning and control framework based on dynamic movement primitives with application to human-robot handovers. Robot. Auton. Syst. 148, 103935 (2022)","journal-title":"Robot. Auton. Syst."},{"key":"27_CR6","unstructured":"Saveriano, M., Abu-Dakka, F.J., Kramberger, A., et al.: Dynamic movement primitives in robotics: A tutorial survey. arXiv 2021, arXiv preprint arXiv:2102.03861"},{"key":"27_CR7","doi-asserted-by":"crossref","unstructured":"Liang, K., Zhao, Z., Yan, D., et al.: Combining Dynamic Movement Primitives and Artificial Potential Fields for Lane Change Obstacle Avoidance Trajectory Planning of Autonomous Vehicles. SAE Technical Paper (2024)","DOI":"10.4271\/2024-01-2567"},{"issue":"2","key":"27_CR8","doi-asserted-by":"publisher","first-page":"5381","DOI":"10.1109\/LRA.2022.3156656","volume":"7","author":"Z Cui","year":"2022","unstructured":"Cui, Z., et al.: Coupled multiple dynamic movement primitives generalization for deformable object manipulation. IEEE Robot. Automation Lett. 7(2), 5381\u20135388 (2022)","journal-title":"IEEE Robot. Automation Lett."},{"issue":"32","key":"27_CR9","doi-asserted-by":"publisher","first-page":"23283","DOI":"10.1007\/s00521-021-05747-8","volume":"35","author":"W Si","year":"2023","unstructured":"Si, W., et al.: Composite dynamic movement primitives based on neural networks for human-robot skill transfer. Neural Comput. Appl. 35(32), 23283\u201323293 (2023)","journal-title":"Neural Comput. Appl."},{"issue":"23","key":"27_CR10","doi-asserted-by":"publisher","first-page":"11184","DOI":"10.3390\/app112311184","volume":"11","author":"A Li","year":"2021","unstructured":"Li, A., Liu, Z., Wang, W., et al.: Reinforcement learning with dynamic movement primitives for obstacle avoidance. Appl. Sci. 11(23), 11184 (2021)","journal-title":"Appl. Sci."},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Liu, Z., Fang, Y.: A superquadrics-based steering angle obstacle avoidance method of DMPs. In: 2023 42nd Chinese Control Conference (CCC), pp. 4273\u20134279. IEEE (2023)","DOI":"10.23919\/CCC58697.2023.10240845"},{"issue":"4","key":"27_CR12","doi-asserted-by":"publisher","first-page":"3979","DOI":"10.1109\/LRA.2019.2930431","volume":"4","author":"\u00c8 Pairet","year":"2019","unstructured":"Pairet, \u00c8., Ard\u00f3n, P., Mistry, M., et al.: Learning generalizable coupling terms for obstacle avoidance via low-dimensional geometric descriptors. IEEE Robot. Automation Lett. 4(4), 3979\u20133986 (2019)","journal-title":"IEEE Robot. Automation Lett."},{"key":"27_CR13","doi-asserted-by":"publisher","unstructured":"Ginesi, M., Meli, D., Calanca, A., Dall\u2019Alba, D., Sansonetto, N., Fiorini, P.: Dynamic movement primitives: Volumetric obstacle avoidance. In: 2019 19th International Conference on Advanced Robotics (ICAR), pp. 234\u2013239 (2019). https:\/\/doi.org\/10.1109\/ICAR46387.2019.8981552","DOI":"10.1109\/ICAR46387.2019.8981552"},{"key":"27_CR14","doi-asserted-by":"crossref","unstructured":"Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: Proceedings of the 1985 IEEE International Conference on Robotics and Automation, vol. 2, pp. 500-505 (1985)","DOI":"10.1109\/ROBOT.1985.1087247"},{"key":"27_CR15","unstructured":"Volpe, R.: Real and artificial forces in the control of manipulators: theory and experiments. Ph.D. thesis, Carnegie Mellon University Department of Physics (1990)"},{"key":"27_CR16","doi-asserted-by":"crossref","unstructured":"Cho, J.H., Pae, D.S., Lim, M.T., et al.: A real-time obstacle avoidance method for autonomous vehicles using an obstacle-dependent Gaussian potential field. J. Adv. Transp., 1\u201315 (2018)","DOI":"10.1155\/2018\/5041401"}],"container-title":["Lecture Notes in Computer Science","Intelligent Robotics and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0789-1_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T19:26:21Z","timestamp":1737660381000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0789-1_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819607884","9789819607891"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0789-1_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"24 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Robotics and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icira2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icira2024.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}