Combined Target-Assignment and Path-Finding problem (TAPF) requires simultaneously assigning targets to agents and planning collision-free paths for agents from their start locations to their assigned targets. As a leading approach to address TAPF, Conflict-Based Search with Target Assignment (CBS-TA) leverages both K-best target assignments to create multiple search trees and Conflict-Based Search (CBS) to resolve collisions in each search tree. While being able to find an optimal solution, CBS-TA suffers from scalability due to the duplicated collision resolution in multiple trees and the expensive computation of K-best assignments. We therefore develop Incremental Target Assignment CBS (ITA-CBS) to bypass these two computational bottlenecks. ITA-CBS generates only a single search tree and avoids computing K-best assignments by incrementally computing new 1-best assignments during the search. We show that, in theory, ITA-CBS is guaranteed to find an optimal solution and, in practice, is computationally efficient.

翻译：联合目标分配与路径规划问题（TAPF）要求同时为智能体分配目标，并规划从起始位置到指定目标的无碰撞路径。作为解决TAPF的领先方法，基于目标分配的冲突搜索（CBS-TA）利用K个最佳目标分配生成多个搜索树，并采用冲突搜索（CBS）在每棵树中解决冲突。尽管能找到最优解，但CBS-TA因多棵树中重复的冲突解决以及K个最佳分配的高计算成本而存在可扩展性问题。为此，我们提出增量式目标分配冲突搜索（ITA-CBS）以绕过这两个计算瓶颈。ITA-CBS仅生成单一搜索树，并通过在搜索过程中增量计算新的1-最佳分配来避免计算K个最佳分配。理论上，我们证明ITA-CBS可保证找到最优解，实际计算中也具有高效性。