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Task allocation and coordinated motion planning for autonomous multi-robot optical inspection systems

Author

Listed:
  • Yinhua Liu

    (University of Shanghai for Science and Technology)

  • Wenzheng Zhao

    (University of Shanghai for Science and Technology)

  • Tim Lutz

    (Virginia Polytechnic Institute and State University)

  • Xiaowei Yue

    (Virginia Polytechnic Institute and State University)

Abstract

Autonomous multi-robot optical inspection systems are increasingly applied for obtaining inline measurements in process monitoring and quality control. Numerous methods for path planning and robotic coordination have been developed for static and dynamic environments and applied to different fields. However, these approaches may not work for the autonomous multi-robot optical inspection system due to fast computation requirements of inline optimization, unique characteristics on robotic end-effector orientations, and complex large-scale free-form product surfaces. This paper proposes a novel task allocation methodology for coordinated motion planning of multi-robot inspection. Specifically, (1) a local robust inspection task allocation is proposed to achieve efficient and well-balanced measurement assignment among robots; (2) collision-free path planning and coordinated motion planning are developed via dynamic searching in robotic coordinate space and perturbation of probe poses or local paths in the conflicting robots. A case study shows that the proposed approach can mitigate the risk of collisions between robots and environments, resolve conflicts among robots, and reduce the inspection cycle time significantly and consistently.

Suggested Citation

  • Yinhua Liu & Wenzheng Zhao & Tim Lutz & Xiaowei Yue, 2022. "Task allocation and coordinated motion planning for autonomous multi-robot optical inspection systems," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2457-2470, December.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:8:d:10.1007_s10845-021-01803-1
    DOI: 10.1007/s10845-021-01803-1
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    References listed on IDEAS

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    1. Xiaotong Hua & Guolei Wang & Jing Xu & Ken Chen, 2021. "Reinforcement learning-based collision-free path planner for redundant robot in narrow duct," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 471-482, February.
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