ITA-LaCAM：一种通过赋值感知的构型空间搜索实现的完备且可扩展的TAPF求解器
ITA-LaCAM: A Complete and Scalable TAPF Solver via Assignment-Aware Configuration-Space Search
- University of Southern California（南加州大学）
- University of California Irvine（加州大学尔湾分校）
- Symbotic（Symbotic公司）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
ITA-LaCAM通过增量修复智能体-目标匹配并利用目标指导PIBT后继生成，实现了无需枚举赋值空间的完备且可扩展的TAPF求解，在9,760个基准实例上达到100%成功率，优于IR-TAPF。
中文摘要 AI 辅助
组合目标分配与路径规划（TAPF）要求在规划无碰撞路径的同时为智能体分配目标。我们提出ITA-LaCAM，一种受LaCAM和ITA-CBS启发的完备且可扩展的TAPF求解器。在ITA-LaCAM中，每个联合构型节点携带一个智能体到目标的匹配。当生成后继节点时，ITA-LaCAM增量地修复移动智能体的匹配，并使用这些目标来指导PIBT后继节点的生成。这种设计能够在无需显式枚举组合赋值空间的情况下实现自适应重新分配，同时保留LaCAM的完备性和可扩展性。在八个地图上包含5至200个智能体的9,760个基准实例中，ITA-LaCAM解决了100%的实例，而使用DBS-Hungarian配置的IR-TAPF解决了95.6%。ITA-LaCAM在84.0%的比较中更快地找到初始解，并在两种方法均解决的实例中，在65.0%的实例中实现了更低的成本总和。
英文摘要
Combined Target Assignment and Path Finding (TAPF) requires assigning targets for agents while simultaneously planning collision-free paths. We present ITA-LaCAM, a complete and scalable TAPF solver inspired by LaCAM and ITA-CBS. In ITA-LaCAM, each joint-configuration node carries an agent-to-target matching. When a successor is generated, ITA-LaCAM incrementally repairs the matching for the agents that moved and uses the targets to guide PIBT successor generation. This design enables adaptive reassignment without explicitly enumerating the combinatorial assignment space, while preserving LaCAM's completeness and scalability. Across 9,760 benchmark instances on eight maps with 5--200 agents, ITA-LaCAM solved 100% of the instances, compared with 95.6% for IR-TAPF configured with DBS-Hungarian. ITA-LaCAM found an initial solution faster in 84.0% of the comparisons and achieved a lower sum of costs in 65.0% of the instances solved by both methods.
