ITA-ECBS: A Bounded-Suboptimal Algorithm for the Combined Target-Assignment and Path-Finding Problem

TL;DR

This paper introduces ITA-ECBS, a bounded-suboptimal algorithm for the combined target-assignment and path-finding problem, improving efficiency over previous methods by using focal search and a new lower bound matrix.

Contribution

It presents the first bounded-suboptimal variant of ITA-CBS, addressing efficiency issues in target assignment and path planning for multi-agent systems.

Findings

ITA-ECBS runs faster than ECBS-TA in 87.42% of test cases.

Uses focal search and a new lower bound matrix for efficiency.

Achieves bounded suboptimality in combined target assignment and path-finding.

Abstract

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% of 54,033 test cases.