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

Year: 2024
Type: preprint
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.
DOI: 10.48550/arxiv.2404.05223
Language: English
Open Access status: green
Awards: CPS: Small: Novel Algorithmic Techniques for Drone Flight Planning on a Large Scale, NSF-BSF:RI:Small:Collaborative Research:Next-Generation Multi-Agent Path Finding Algorithms, AI Institute for Advances in Optimization, NSF-BSF: RI: Small: Efficient Bi- and Multi-Objective Search Algorithms, Development of a Modular, Scalable, and Extensible Model-Based Systems Engineering Advanced Manufacturing Curriculum, CISE: Large: Causal Foundations for Decision Making and Learning
OpenAlex ID: W4394673086
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Cited by: 1
Related to: 10