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。本文提出一种面向MAMP的时空冲突消解方法。我们采用新颖且灵活的球体离散化方法对轨迹进行建模。该方法基于深度优先冲突搜索策略，既能通过解耦方法实现可扩展性，又能保持耦合方法的计算保证。我们设计了规避离散化误差并满足生成解中运动学约束的流程。理论上，我们证明了算法的连续时间可行性与构造空间完备性。实验表明，本算法在运行时间与解质量方面均达到当前最优水平，且通过兼容静态与动态障碍物拓展了现有工作的能力。我们使用开源车辆仿真器CARLA在多种无信号灯交叉口场景中验证算法，结果表明，在涉及网联与非网联车辆的空间约束场景下，成功率显著提升。此外，我们维持了合理的次优比，该比值在日益复杂的场景中仍具有良好可扩展性。