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Neural motion planning in dynamic environments
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Artificial Intelligence.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.ORCID iD: 0000-0002-2678-1330
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Artificial Intelligence.ORCID iD: 0000-0001-5183-234X
ABB Robotics.
2023 (English)In: IFAC-PapersOnLine, Elsevier, 2023, p. 10126-10131Conference paper, Published paper (Refereed)
Abstract [en]

Motion planning is a mature field within robotics with many successful solutions. Despite this, current state-of-the-art planners are still computationally heavy. To address this, recent work have employed ideas from machine learning, which have drastically reduced the computational cost once a planner has been trained. It is mainly static environments that have been studied in this way. We continue along the same research direction but expand the problem to include dynamic environments, hence increasing the difficulty of the problem. Analogously to previous work, we use imitation learning, where a planning policy is learnt from an expert planner in a supervised manner. Our main contribution is a planner mimicking an expert that considers the future movement of all the obstacles in the environment, which is key in order to learn a successful policy in dynamic environments. We illustrate this by evaluating our approach in a dynamic environment and by comparing our planner with a conventional planner that re-plans at every iteration, which is a common approach in dynamic motion planning. We observe that our approach yields a higher success rate, while also taking less time and accumulating less distance to reach the goal.

Place, publisher, year, edition, pages
Elsevier, 2023. p. 10126-10131
Keywords [en]
Data-driven control, Learning for control, Robots manipulators, Motion planning, Imitation learning
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-518375DOI: 10.1016/j.ifacol.2023.10.885OAI: oai:DiVA.org:uu-518375DiVA, id: diva2:1820717
Conference
IFAC World Congress
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Available from: 2023-12-18 Created: 2023-12-18 Last updated: 2023-12-24Bibliographically approved

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Wullt, BernhardMattsson, PerSchön, Thomas B.

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