无需领导者的分层搜索法，让多辆无人车高效避障协同行驶
Hierarchical Search-Based Cooperative Motion Planning
- 分层设计：高层用二叉冲突搜索树提速，底层生成符合运动约束的路径
- 支持多组不同形状、有异常车辆和复杂障碍物的场景，路径零碰撞
- 实测与仿真均验证有效，代码已开源，适合机器人协同场景研究者
协同路径规划是多智能体系统研究的关键，广泛应用于军事、农业和工业领域。现有算法常受限于简化的运动模型及对多组场景支持不足。针对非完整阿克曼模型的地面无人车（UGV），本文提出一种无领导者、分层的基于搜索的协同运动规划（SCMP）方法。高层采用二叉冲突搜索树以降低运行时间，低层生成满足运动学约束且无碰撞的路径，同时具备形状约束能力。该方法可适应多组不同形状、存在异常个体及复杂障碍物的场景。通过算法对比、性能测试、仿真与真实环境实验，验证了方法的有效性与适用性。代码将开源至 https://github.com/WYCUniverStar/SCMP。
原文摘要 · Abstract (English)
Cooperative path planning, a crucial aspect of multi-agent systems research, serves a variety of sectors, including military, agriculture, and industry. Many existing algorithms, however, come with certain limitations, such as simplified kinematic models and inadequate support for multiple group scenarios. Focusing on the planning problem associated with a nonholonomic Ackermann model for Unmanned Ground Vehicles (UGV), we propose a leaderless, hierarchical Search-Based Cooperative Motion Planning (SCMP) method. The high-level utilizes a binary conflict search tree to minimize runtime, while the low-level fabricates kinematically feasible, collision-free paths that are shape-constrained. Our algorithm can adapt to scenarios featuring multiple groups with different shapes, outlier agents, and elaborate obstacles. We conduct algorithm comparisons, performance testing, simulation, and real-world testing, verifying the effectiveness and applicability of our algorithm. The implementation of our method will be open-sourced at https://github.com/WYCUniverStar/SCMP.
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