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Article: Adaptive Motion Planning for a Collaborative Robot Based on Prediction Uncertainty to Enhance Human Safety and Work Efficiency

TitleAdaptive Motion Planning for a Collaborative Robot Based on Prediction Uncertainty to Enhance Human Safety and Work Efficiency
Authors
Keywordslearning and adaptive systems
collision avoidance
industrial robot
cognitive human-robot interaction
Adaptive control
Issue Date2019
Citation
IEEE Transactions on Robotics, 2019, v. 35, n. 4, p. 817-832 How to Cite?
AbstractIndustrial robots are expected to share the same workspace with human workers and work in cooperation with humans to improve the productivity and maintain the quality of products. In this situation, the worker's safety and work-time efficiency must be enhanced simultaneously. In this paper, we extend a task scheduling system proposed in the previous work by installing an online trajectory generation system. On the basis of the probabilistic prediction of the worker's motion and the receding horizon scheme for the trajectory planning, the proposed motion planning system calculates an optimal trajectory that realizes collision avoidance and the reduction of waste time simultaneously. Moreover, the proposed system plans the robot's trajectory adaptively based on updated predictions and its uncertainty to deal not only with the regular behavior of workers but also with their irregular behavior. We apply the proposed system to an assembly process where a two-link planar manipulator supports a worker by delivering parts and tools. After implementing the proposed system, we experimentally evaluate the effectiveness of the adaptive motion planning system.
Persistent Identifierhttp://hdl.handle.net/10722/303011
ISSN
2023 Impact Factor: 9.4
2023 SCImago Journal Rankings: 3.669
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorKanazawa, Akira-
dc.contributor.authorKinugawa, Jun-
dc.contributor.authorKosuge, Kazuhiro-
dc.date.accessioned2021-09-07T08:43:01Z-
dc.date.available2021-09-07T08:43:01Z-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Robotics, 2019, v. 35, n. 4, p. 817-832-
dc.identifier.issn1552-3098-
dc.identifier.urihttp://hdl.handle.net/10722/303011-
dc.description.abstractIndustrial robots are expected to share the same workspace with human workers and work in cooperation with humans to improve the productivity and maintain the quality of products. In this situation, the worker's safety and work-time efficiency must be enhanced simultaneously. In this paper, we extend a task scheduling system proposed in the previous work by installing an online trajectory generation system. On the basis of the probabilistic prediction of the worker's motion and the receding horizon scheme for the trajectory planning, the proposed motion planning system calculates an optimal trajectory that realizes collision avoidance and the reduction of waste time simultaneously. Moreover, the proposed system plans the robot's trajectory adaptively based on updated predictions and its uncertainty to deal not only with the regular behavior of workers but also with their irregular behavior. We apply the proposed system to an assembly process where a two-link planar manipulator supports a worker by delivering parts and tools. After implementing the proposed system, we experimentally evaluate the effectiveness of the adaptive motion planning system.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Robotics-
dc.subjectlearning and adaptive systems-
dc.subjectcollision avoidance-
dc.subjectindustrial robot-
dc.subjectcognitive human-robot interaction-
dc.subjectAdaptive control-
dc.titleAdaptive Motion Planning for a Collaborative Robot Based on Prediction Uncertainty to Enhance Human Safety and Work Efficiency-
dc.typeArticle-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1109/TRO.2019.2911800-
dc.identifier.scopuseid_2-s2.0-85070437289-
dc.identifier.volume35-
dc.identifier.issue4-
dc.identifier.spage817-
dc.identifier.epage832-
dc.identifier.eissn1941-0468-
dc.identifier.isiWOS:000480360700002-

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