Multi-agent motion planning (MAMP) is a critical challenge in applications such as connected autonomous vehicles and multi-robot systems. In this paper, we propose a space-time conflict resolution approach for MAMP. We formulate the problem using a novel, flexible sphere-based discretization for trajectories. Our approach leverages a depth-first conflict search strategy to provide the scalability of decoupled approaches while maintaining the computational guarantees of coupled approaches. We compose procedures for evading discretization error and adhering to kinematic constraints in generated solutions. Theoretically, we prove the continuous-time feasibility and formulation-space completeness of our algorithm. Experimentally, we demonstrate that our algorithm matches the performance of the current state of the art with respect to both runtime and solution quality, while expanding upon the abilities of current work through accommodation for both static and dynamic obstacles. We evaluate our algorithm in various unsignalized traffic intersection scenarios using CARLA, an open-source vehicle simulator. Results show significant success rate improvement in spatially constrained settings, involving both connected and non-connected vehicles. Furthermore, we maintain a reasonable suboptimality ratio that scales well among increasingly complex scenarios.

翻译：多智能体运动规划（MAMP）是网联自动驾驶车辆与多机器人系统等应用中的关键挑战。本文提出了一种面向MAMP的时空冲突解决框架。我们采用基于球体的新型灵活离散化方法对轨迹进行建模，通过深度优先冲突搜索策略实现解耦方法的高扩展性，同时保持耦合方法的计算保障特性。我们设计了一套流程以规避离散化误差，并确保生成的解满足运动学约束。理论上，我们证明了算法的连续时间可行性与公式空间完备性。实验表明，本算法在运行时间与求解质量方面均达到当前最优水平，同时通过兼容静态与动态障碍物拓展了现有方法的能力。我们利用开源车辆仿真器CARLA在多种无信号交通路口场景中评估算法，结果显示在涉及网联与非网联车辆的空间约束场景中，成功率显著提升。此外，我们保持了合理的次优性比率，该比率在日益复杂的场景中仍具有良好可扩展性。