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：组合目标分配与路径规划问题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。作为解决该问题的前沿方法，基于目标分配的冲突导向搜索通过利用K个最优目标分配构建多个搜索树，并采用冲突导向搜索在每个搜索树中解决冲突。尽管CBS-TA能够找到最优解，但由于多棵树中重复的冲突消解以及K最优分配的高昂计算成本，其可扩展性受限。为此，我们提出增量式目标分配CBS以绕过这两个计算瓶颈。ITA-CBS仅生成单棵搜索树，并通过在搜索过程中增量计算新的1最优分配来避免K最优分配的计算。理论证明ITA-CBS保证能求得最优解，且实际计算效率显著。