多智能体运动规划中同时计算多种优先级，提升效率与效果。
Simultaneous Computation with Multiple Prioritizations in Multi-Agent Motion Planning
- 通过并行计算多种优先级策略，避免逐次迭代找最优
- 在十辆车道路实验中实现实时运行，接近最优解
- 适用于对实时性要求高的交通系统与复杂动态场景
大规模网络中的多智能体路径规划（MAPF）计算成本高。现有优先级规划（PP）方法依赖启发式或反复迭代来确定优先级，前者泛化性差，后者耗时。本文提出一种新方法，让智能体能同时计算多个优先级策略，无需领域知识。研究聚焦于带滚动时域的多智能体运动规划（MAMP），考虑系统动态特性，比传统MAPF更精确。在数值实验中，该方法性能接近最优优先级，优于现有先进方法，仅小幅增加计算时间。在包含十辆车辆的真实道路网络实验中，验证了其在本实验室平台上的实时可行性。
原文摘要 · Abstract (English)
Multi-agent path finding (MAPF) in large networks is computationally challenging. An approach for MAPF is prioritized planning (PP), in which agents plan sequentially according to their priority. Albeit a computationally efficient approach for MAPF, the solution quality strongly depends on the prioritization. Most prioritizations rely either on heuristics, which do not generalize well, or iterate to find adequate priorities, which costs computational effort. In this work, we show how agents can compute with multiple prioritizations simultaneously. Our approach is general as it does not rely on domain-specific knowledge. The context of this work is multi-agent motion planning (MAMP) with a receding horizon subject to computation time constraints. MAMP considers the system dynamics in more detail compared to MAPF. In numerical experiments on MAMP, we demonstrate that our approach to prioritization comes close to optimal prioritization and outperforms state-of-the-art methods with only a minor increase in computation time. We show real-time capability in an experiment on a road network with ten vehicles in our Cyber-Physical Mobility Lab.
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