ESCoT: An Enhanced Step-based Coordinate Trajectory Planning Method for Multiple Car-like Robots
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912535949606912 |
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| author | Jiang, Junkai Chen, Yihe Yang, Yibin Li, Ruochen Xu, Shaobing Wang, Jianqiang |
| author_facet | Jiang, Junkai Chen, Yihe Yang, Yibin Li, Ruochen Xu, Shaobing Wang, Jianqiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09581 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ESCoT: An Enhanced Step-based Coordinate Trajectory Planning Method for Multiple Car-like Robots Jiang, Junkai Chen, Yihe Yang, Yibin Li, Ruochen Xu, Shaobing Wang, Jianqiang Robotics 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. |
| title | ESCoT: An Enhanced Step-based Coordinate Trajectory Planning Method for Multiple Car-like Robots |
| topic | Robotics |
| url | https://arxiv.org/abs/2508.09581 |