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

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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.

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Acknowledgements

Dr. Liu’s research was partially funded by National Science Foundation of China (51875362) and State Key Laboratory of Mechanical System and Vibration (MSV202010).

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Authors and Affiliations

  1. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Yinhua Liu & Wenzheng Zhao

  2. Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA

    Tim Lutz & Xiaowei Yue

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  1. Yinhua Liu
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  2. Wenzheng Zhao
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  3. Tim Lutz
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  4. Xiaowei Yue
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Correspondence to Xiaowei Yue.

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Liu, Y., Zhao, W., Lutz, T. et al. Task allocation and coordinated motion planning for autonomous multi-robot optical inspection systems. J Intell Manuf 33, 2457–2470 (2022). https://doi.org/10.1007/s10845-021-01803-1

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  • DOI: https://doi.org/10.1007/s10845-021-01803-1

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