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arXiv · 2508.09581

ESCoT: An Enhanced Step-based Coordinate Trajectory Planning Method for Multiple Car-like Robots

Junkai Jiang·Yihe Chen·Yibin Yang·Ruochen Li·Shaobing Xu·Jianqiang Wang
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Abstract

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.

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Junkai Jiang, Yihe Chen, Yibin Yang, Ruochen Li, Shaobing Xu, Jianqiang Wang. 2025-08-13. ESCoT: An Enhanced Step-based Coordinate Trajectory Planning Method for Multiple Car-like Robots. https://arxiv.org/abs/2508.09581

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