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Object Placement Planning and optimization for Robot Manipulators
KTH Royal Institute of Technology, Division of Robotics, Perception and Learning (RPL), CAS, CSC, Stockholm, Sweden.
Yale University, Department of Mechanical Engineering and Material Science, New Haven, United States.
Örebro University, School of Science and Technology. (Center for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0003-3958-6179
KTH Royal Institute of Technology, Division of Robotics, Perception and Learning (RPL), CAS, CSC, Stockholm, Sweden.
2019 (English)In: IEEE International Conference on Intelligent Robots and Systems, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 7417-7424Conference paper, Published paper (Refereed)
Abstract [en]

We address the problem of planning the placement of a rigid object with a dual-arm robot in a cluttered environment. In this task, we need to locate a collision-free pose for the object that a) facilitates the stable placement of the object, b) is reachable by the robot and c) optimizes a user-given placement objective. In addition, we need to select which robot arm to perform the placement with. To solve this task, we propose an anytime algorithm that integrates sampling-based motion planning with a novel hierarchical search for suitable placement poses. Our algorithm incrementally produces approach motions to stable placement poses, reaching placements with better objective as runtime progresses. We evaluate our approach for two different placement objectives, and observe its effectiveness even in challenging scenarios.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 7417-7424
Keywords [en]
C (programming language), Intelligent robots, Manipulators, Motion planning, Robot applications, Robotic arms, Any-time algorithms, Cluttered environments, Collision-free, Dual-arm robot, Hierarchical search, Object placement, Robot manipulator, Sampling-based motion planning, Robot programming
National Category
Robotics
Identifiers
URN: urn:nbn:se:oru:diva-80794DOI: 10.1109/IROS40897.2019.8967732Scopus ID: 2-s2.0-85081167161ISBN: 9781728140049 (print)OAI: oai:DiVA.org:oru-80794DiVA, id: diva2:1416708
Conference
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 3-8 Nov., 2019.
Available from: 2020-03-25 Created: 2020-03-25 Last updated: 2020-03-25Bibliographically approved

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Stork, Johannes Andreas

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
  • Other style
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  • de-DE
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Output format
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