ITA-ECBS: A Bounded-Suboptimal Algorithm for the Combined Target-Assignment and Path-Finding Problem
arxiv(2024)
摘要
Multi-Agent Path Finding (MAPF), i.e., finding collision-free paths for
multiple robots, plays a critical role in many applications. Sometimes,
assigning a target to each agent also presents a challenge. The Combined
Target-Assignment and Path-Finding (TAPF) problem, a variant of MAPF, requires
one to simultaneously assign targets to agents and plan collision-free paths
for agents. Several algorithms, including CBM, CBS-TA, and ITA-CBS, optimally
solve the TAPF problem, with ITA-CBS being the leading algorithm for minimizing
flowtime. However, the only existing bounded-suboptimal algorithm ECBS-TA is
derived from CBS-TA rather than ITA-CBS. So, it faces the same issues as
CBS-TA, such as searching through multiple constraint trees and spending too
much time on finding the next-best target assignment. We introduce ITA-ECBS,
the first bounded-suboptimal variant of ITA-CBS. Transforming ITA-CBS to its
bounded-suboptimal variant is challenging because different constraint tree
nodes can have different assignments of targets to agents. ITA-ECBS uses focal
search to achieve efficiency and determines target assignments based on a new
lower bound matrix. We show that it runs faster than ECBS-TA in 87.42
54,033 test cases.
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