论文标题
最佳和有界的多目标任务分配和路径查找
Optimal and Bounded-Suboptimal Multi-Goal Task Assignment and Path Finding
论文作者
论文摘要
我们从理论和算法的观点正式化和研究多目标任务分配和路径发现（MG-TAPF）问题。 MG-TAPF问题是要计算到代理的任务分配，其中每个任务都由一系列目标位置组成，并为访问其分配任务的所有目标位置的代理的无碰撞路径组成。从理论上讲，我们证明MG-TAPF问题是最佳解决的NP问题。我们介绍了基于多代理路径问题的算法技术构建的算法，并以最佳的和有限的方式解决了MG-TAPF问题。我们通过实验将这些算法在各种不同的基准域上进行比较。
We formalize and study the multi-goal task assignment and path finding (MG-TAPF) problem from theoretical and algorithmic perspectives. The MG-TAPF problem is to compute an assignment of tasks to agents, where each task consists of a sequence of goal locations, and collision-free paths for the agents that visit all goal locations of their assigned tasks in sequence. Theoretically, we prove that the MG-TAPF problem is NP-hard to solve optimally. We present algorithms that build upon algorithmic techniques for the multi-agent path finding problem and solve the MG-TAPF problem optimally and bounded-suboptimally. We experimentally compare these algorithms on a variety of different benchmark domains.
