Multi-Agent Path Finding (MAPF) seeks collision-free paths for multiple agents from their respective start locations to their respective goal locations while minimizing path costs. Most existing MAPF algorithms rely on a common assumption of synchronized actions, where the actions of all agents start at the same time and always take a time unit, which may limit the use of MAPF planners in practice. To get rid of this assumption, Continuous-time Conflict-Based Search (CCBS) is a popular approach that can find optimal solutions for MAPF with asynchronous actions (MAPF-AA). However, CCBS has recently been identified to be incomplete due to an uncountably infinite state space created by continuous wait durations. This paper proposes a new method, Conflict-Based Search with Asynchronous Actions (CBS-AA), which bypasses this theoretical issue and can solve MAPF-AA with completeness and solution optimality guarantees. Based on CBS-AA, we also develop conflict resolution techniques to improve the scalability of CBS-AA further. Our test results show that our method can reduce the number of branches by up to 90%.

翻译：多智能体路径规划（MAPF）旨在为多个智能体规划从各自起点到终点的无碰撞路径，同时最小化路径成本。现有大多数MAPF算法依赖于同步动作的常见假设，即所有智能体的动作同时启动且始终占用单位时间，这限制了MAPF规划器在实际中的应用。为摆脱这一假设，连续时间冲突搜索（CCBS）是一种流行的方案，可针对异步动作的MAPF（MAPF-AA）找到最优解。然而，近期研究发现CCBS因连续等待时长导致的不可数无限状态空间而存在不完备性。本文提出一种新方法——基于冲突的异步动作搜索（CBS-AA），该方法规避了这一理论问题，能在保证完备性和解最优性的条件下求解MAPF-AA。基于CBS-AA，我们还开发了冲突解决技术以进一步提升其可扩展性。实验结果表明，我们的方法可将分支数量减少高达90%。