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<p>In high-mix industrial environments, where task requirements change frequently, motion planners must adapt rapidly without extensive retraining or manual redesign. This study presents a state machine construction method that incorporates the previously proposed Path-Reuse (PR) algorithm. PR stores past robot trajectories and efficiently adapts them to new start–goal pairs with minimal computation, enabling the autonomous generation of safe and executable paths for articulated robots. By leveraging PR, the proposed method eliminates both the large datasets and expert intervention that are often required by learning-based approaches. Experiments in dynamically changing work cells compared the method with state machines that employ conventional planners such as RRT-connect and STOMP. The state machine incorporating the PR method significantly shortened planning times and increased success rates in collision-rich conditions.</p>

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Path Reuse-based state machine for fast and safe motion planning in autonomous industrial robots

  • 期刊论文

  • 发表日期：

    2025年8月16日

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  • Soma Fumoto,
  • Tsubasa Watanabe,
  • Takeshi Nishida

摘要

In high-mix industrial environments, where task requirements change frequently, motion planners must adapt rapidly without extensive retraining or manual redesign. This study presents a state machine construction method that incorporates the previously proposed Path-Reuse (PR) algorithm. PR stores past robot trajectories and efficiently adapts them to new start–goal pairs with minimal computation, enabling the autonomous generation of safe and executable paths for articulated robots. By leveraging PR, the proposed method eliminates both the large datasets and expert intervention that are often required by learning-based approaches. Experiments in dynamically changing work cells compared the method with state machines that employ conventional planners such as RRT-connect and STOMP. The state machine incorporating the PR method significantly shortened planning times and increased success rates in collision-rich conditions.