This paper introduces an innovative multi-agent path finding (MAPF) system specifically designed for navigating multi-Ackerman robotic systems in intricate environments. The Mars Planner, the proposed solution, enhances path planning by tackling collision-free path challenges encountered by groups of intelligent agents. Our contributions include the development of two key algorithms: the Fast Batch Path Finding (FBPF) and the Batch Spatio-Temporal Path Refinement (BSTPR). FBPF utilizes a hybrid A* approach to generate preliminary coarse paths within free configuration spaces, while BSTPR refines these paths using topological homotopy strategies to optimize time allocation and effectively resolve internal conflicts. Through simulations and physical experiments, we demonstrate significant enhancements in computational efficiency and path quality compared to existing methods. In conclusion, the Mars Planner stands as an efficient solution capable of managing large-scale complexity in real-world applications. It offers a robust and scalable framework suitable for diverse environments and scenarios.


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    Title :

    Mars Planner: Improved Batch Spatio-Temporal Path Planning for Multi-Ackerman Robotic Systems


    Contributors:
    Guo, Song (author) / Wang, Shen'ao (author) / He, Junjie (author) / Chen, Liming (author) / Wang, Hang (author) / Sun, Hongbin (author)


    Publication date :

    2024-09-24


    Size :

    4567232 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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