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

-- 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. Research on multi-robot systems (MRSs) has attracted growing attention in recent years, for its capability of accomplishing complicated missions more effectively and efficiently [1], [2]. MRSs involve many critical problems, one of which is multi-agent path finding (MAPF). MAPF focuses on finding conflict-free paths for multiple agents in a shared environment, ensuring that each agent can navigate from its start position to its goal without colliding with others [3].