Year: 2022
Type: conference-paper
Abstract: 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.
Authors: Xinyi Zhong, Jiaoyang Li, Sven Koenig, Hang Ma
Institutions: Simon Fraser University, University of Southern California
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G2090803145, Intelligent Coordination for Multi-Agent Systems, G2904675200, G3503992046, G4260377764, 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, S&AS: FND: Long-Term Planning and Robust Plan Execution for Multi-Robot Systems, G7760752320, Development of a Modular, Scalable, and Extensible Model-Based Systems Engineering Advanced Manufacturing Curriculum, G8779139627, RI: Medium: Collaborative Research: Experience-Based Planning: A Framework for Lifelong Planning
OpenAlex ID: W4285022255