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

2025-08-13

AI Summary

Existing multi-vehicle trajectory planning (MVTP) methods suffer from low success rates and poor path quality in both sparse and dense robotic environments. To address this, we propose an enhanced stepwise cooperative trajectory planning framework. Our method innovatively integrates local robot-group collaborative optimization with an adaptive replanning mechanism triggered by repeated configuration detection, all embedded within a stepwise coordinate-trajectory formulation that incorporates dynamic conflict detection and localized cooperative optimization. Compared to conventional stepwise approaches, our method significantly improves algorithmic robustness and trajectory continuity: path quality improves by up to 70% in sparse scenarios, by 34% on average in random settings, and success rate remains above 50% even in highly dense configurations. Extensive real-world experiments with robotic swarms validate the method’s practicality, scalability, and effectiveness across diverse operational conditions.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Enhances trajectory planning for multiple car-like robots
• Improves solution quality in sparse and dense scenarios
• Ensures high success rate in challenging configurations

Innovation

• Collaborative planning for local robot groups
• Replanning for duplicate configurations
• Enhanced step-based coordinate trajectory planning