提升多车路径规划效率，解决复杂场景下的冲突问题
ESCoT: An Enhanced Step-based Coordinate Trajectory Planning Method for Multiple Car-like Robots
- 分组协作与重复配置重规划双策略优化路径生成
- 稀疏场景下冲突解决效率最高提升70%，密集场景成功率超50%
- 适用于真实机器人系统，兼顾性能与实时性
多车路径规划（MVTP）是多机器人系统（MRS）中的关键挑战，应用广泛。本文提出增强型基于步长的坐标轨迹规划方法ESCoT，引入局部机器人组协作规划与重复配置重规划两项策略，显著提升步长式MVTP方法性能。实验表明，ESCoT在稀疏场景下相比基线方法显著提升解决方案质量，在典型冲突场景中效率最高提升70%，随机场景中提升34%，同时保持高效求解；在密集场景下优于所有基线方法，最困难配置下成功率仍超50%。结果证明ESCoT能有效解决MVTP问题，拓展了步长式方法的能力边界。实际机器人测试验证了算法在真实场景中的适用性。
原文摘要 · Abstract (English)
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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