A General Formal Framework for Pathfinding Problems with Multiple Agents

Author:

Erdem Esra,Kisa Doga,Oztok Umut,Schüller Peter

Abstract

Pathfinding for a single agent is the problem of planning a route from an initial location to a goal location in an environment, going around obstacles. Pathfinding for multiple agents also aims to plan such routes for each agent, subject to different constraints, such as restrictions on the length of each path or on the total length of paths, no self-intersecting paths, no intersection of paths/plans, no crossing/meeting each other. It also has variations for finding optimal solutions, e.g., with respect to the maximum path length, or the sum of plan lengths. These problems are important for many real-life applications, such as motion planning, vehicle routing, environmental monitoring, patrolling, computer games. Motivated by such applications, we introduce a formal framework that is general enough to address all these problems: we use the expressive high-level representation formalism and efficient solvers of the declarative programming paradigm Answer Set Programming. We also introduce heuristics to improve the computational efficiency and/or solution quality. We show the applicability and usefulness of our framework by experiments, with randomly generated problem instances on a grid, on a real-world road network, and on a real computer game terrain.

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Subject

General Medicine

Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Efficient Heuristics for Multi-Robot Path Planning in Crowded Environments;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01

2. Multi-Agent Path Finding for Non-Conflicting Paths: An Infinite Speed Conflict-Based Search Approach;2023 42nd Chinese Control Conference (CCC);2023-07-24

3. Toward Efficient Physical and Algorithmic Design of Automated Garages;2023 IEEE International Conference on Robotics and Automation (ICRA);2023-05-29

4. Flexible Route Planning for Multiple Mobile Robots by Combining Q–Learning and Graph Search Algorithm;Applied Sciences;2023-01-31

5. Conflict-Based Search for Optimal Multi-Agent Pathfinding;International Journal of Advanced Research in Science, Communication and Technology;2023-01-30

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