Multi-Agent Path Finding (MAPF), i.e., finding collision-free paths for multiple robots, plays a critical role in many applications. Sometimes, assigning a specific target to each agent also presents a challenge. The Combined Target-Assignment and Path-Finding (TAPF) problem, a variant of MAPF, requires simultaneously assigning targets to agents and planning collision-free paths. Several algorithms, including CBM, CBS-TA, and ITA-CBS, can optimally solve the TAPF problem, with ITA-CBS being the leading method of flowtime. However, the only existing suboptimal method ECBS-TA, is derived from CBS-TA rather than ITA-CBS, and adapting the optimal ITA-CBS method to its bounded-suboptimal variant is a challenge due to the variability of target assignment solutions in different search nodes. We introduce ITA-ECBS as the first bounded-suboptimal variant of ITA-CBS. ITA-ECBS employs focal search to enhance efficiency and determines target assignments based on a new lower bound matrix. We show that ITA-ECBS outperforms the baseline method ECBS-TA in 87.42% of 54,033 test cases.

翻译：多智能体路径规划（MAPF），即为多个机器人寻找无碰撞路径，在许多应用中起着关键作用。有时，为每个智能体分配特定目标也是一项挑战。组合目标分配与路径规划（TAPF）问题作为MAPF的变体，需要同时为智能体分配目标并规划无碰撞路径。包括CBM、CBS-TA和ITA-CBS在内的多种算法可最优地解决TAPF问题，其中ITA-CBS在流程时间方面处于领先地位。然而，现有的唯一次优方法ECBS-TA源于CBS-TA而非ITA-CBS，由于不同搜索节点中目标分配解决方案的变异性，将最优的ITA-CBS方法改编为其有界次优变体是一项挑战。我们引入ITA-ECBS作为ITA-CBS的首个有界次优变体。ITA-ECBS采用焦点搜索以提高效率，并基于新的下界矩阵确定目标分配。我们证明，在54,033个测试案例中，ITA-ECBS在87.42%的情况下优于基线方法ECBS-TA。