ESCoT: 複数の車型ロボットのための拡張ステップベース協調軌道計画法
ESCoT: An Enhanced Step-based Coordinate Trajectory Planning Method for Multiple Car-like Robots
複数の車型ロボットの軌道計画において、局所的な協調計画と重複構成の再計画を組み合わせることで、ステップベース手法の性能を向上させた。
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著者: Junkai Jiang, Yihe Chen, Yibin Yang, Ruochen Li, Shaobing Xu, Jianqiang Wang
分類: cs.RO
原文アブストラクト
Multi-vehicle trajectory planning (MVTP) is one of the key challenges in multi-robot systems (MRSs) and has broad applications across various fields. This paper presents ESCoT, an enhanced step-based coordinate trajectory planning method for multiple car-like robots. ESCoT incorporates two key strategies: collaborative planning for local robot groups and replanning for duplicate configurations. These strategies effectively enhance the performance of step-based MVTP methods. Through extensive experiments, we show that ESCoT 1) in sparse scenarios, significantly improves solution quality compared to baseline step-based method, achieving up to 70% improvement in typical conflict scenarios and 34% in randomly generated scenarios, while maintaining high solving efficiency; and 2) in dense scenarios, outperforms all baseline methods, maintains a success rate of over 50% even in the most challenging configurations. The results demonstrate that ESCoT effectively solves MVTP, further extending the capabilities of step-based methods. Finally, practical robot tests validate the algorithm's applicability in real-world scenarios.
