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<p>This paper presents a new approach to the multi-agent task assignment and motion planning problem, paying particular attention to the dynamic characteristics of multiple mobile robots such as acceleration and deceleration patterns. By utilizing the Conflict-Based Search with Task Assignment framework, the algorithm is composed of three layers (upper-layer: initial and re-plan, middle-layer: task assignment, and lower-layer: motion planning) which can efficiently handle multiple tasks and robots and deal with a variety of speed and motion constraints. The proposed algorithm was evaluated by simulation and compared to existing state-of-the-art methods such as cooperative A* and priority-based search. Using the proposed algorithm, we obtained superior solutions with makespans of 34–58% shorter than those of the existing methods, while maintaining manageable computation time, even in complex scenarios involving many robots and obstacles. This study contributes to the advancement of Artificial Intelligence by developing a sophisticated algorithm like advanced search tree methods.</p>

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Pickup and delivery problem solver for multiple mobile robots considering robot’s dynamics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月5日

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  • Tomoaki Shimizu,
  • Ayumu Goto,
  • Kosuke Taneda,
  • Takeshi Muranaka,
  • Yuji Enoki,
  • Toyokazu Kobayashi,
  • Tomoya Hattori,
  • Ryota Takamido,
  • Jun Ota

摘要

This paper presents a new approach to the multi-agent task assignment and motion planning problem, paying particular attention to the dynamic characteristics of multiple mobile robots such as acceleration and deceleration patterns. By utilizing the Conflict-Based Search with Task Assignment framework, the algorithm is composed of three layers (upper-layer: initial and re-plan, middle-layer: task assignment, and lower-layer: motion planning) which can efficiently handle multiple tasks and robots and deal with a variety of speed and motion constraints. The proposed algorithm was evaluated by simulation and compared to existing state-of-the-art methods such as cooperative A* and priority-based search. Using the proposed algorithm, we obtained superior solutions with makespans of 34–58% shorter than those of the existing methods, while maintaining manageable computation time, even in complex scenarios involving many robots and obstacles. This study contributes to the advancement of Artificial Intelligence by developing a sophisticated algorithm like advanced search tree methods.