Robust multi-robot coordination in pick-and-place tasks based on part-dispatching rules |
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| Affiliation: | 1. School of Mechanical and Automotive Engineering, South China University of Technology (SCUT), 381 Wushan, Tianhe, Guangzhou, Guangdong, 510640, China;2. Research into Artifacts, Center for Engineering (RACE), The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan;3. Faculty of System Design, Tokyo Metropolitan University, 6-6, Asahigaoka, Hino-shi, Tokyo 191-0065, Japan;4. Department of Mechanical Engineering, College of Engineering, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo 135-8548, Japan;5. DENSO WAVE Inc., 1-1 Yoshiike, Kusaki, Agui-cho, Chita-gun, Aichi 470-2297, Japan;1. Research Institute of Ophthalmology, Cairo, Egypt;2. Division of Ophthalmology, Department of Clinical Neurosciences, Geneva University Hospitals, Geneva, Switzerland;3. Department of Ophthalmology, Keck School of Medicine, University of Southern California, Los Angeles, California States;1. Department of Computer Science, School of Information Science and Engineering, Xiamen University, China;2. School of Computing, National University of Singapore, 13 Computing Drive, Singapore;1. Department of Engineering, University of Perugia, via Duranti 93, 06125, Perugia, Italy;2. Fondazione Bruno Kessler, via Sommarive 18, 38123, Povo, Trento, Italy |
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| Abstract: | This paper addresses the problem of realizing multi-robot coordination that is robust against pattern variation in a pick-and-place task. To improve productivity and reduce the number of parts remaining on the conveyor, a robust and appropriate part flow and multi-robot coordinate strategy are needed. We therefore propose combining part-dispatching rules to coordinate robots, by integrating a greedy randomized adaptive search procedure (GRASP) and a Monte Carlo strategy (MCS). GRASP is used to search for the appropriate combination of part-dispatching rules, and MCS is used to estimate the minimum-maximal part flow for one combination of part-dispatching rules. The part-dispatching rule of first-in–first-out is used to control the final robot in the multi-robot system to pick up parts left by other robots, and the part-dispatching rule of shortest processing time is used to make the other robots pick up as many parts as possible. By comparing it with non-cooperative game theory, we verify that the appropriate combination of part-dispatching rules is effective in improving the productivity of a multi-robot system. By comparing it with a genetic algorithm, we also verify that MCS is effective in estimating minimum-maximal part flow. The task-completion success rate derived via the proposed method reached 99.4% for 10,000 patterns. |
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| Keywords: | Part-dispatching rule Multi-robot system Pattern variation Pick-and-place task Greedy randomized adaptive search procedure Monte Carlo strategy |
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