Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios
Hang Ma, Sven Koenig, Nora Ayanian, Liron Cohen, Wolfgang Hoenig, T., K. Satish Kumar, Tansel Uras, Hong Xu, Craig Tovey, Guni Sharon
TL;DR
This paper discusses the challenges and research directions for adapting multi-agent path finding (MAPF) techniques to real-world scenarios, emphasizing the importance of addressing practical issues over merely improving algorithm speed.
Contribution
It highlights key issues in applying MAPF to real-world problems and proposes four research directions to tackle these challenges.
Findings
Identifies practical issues in real-world MAPF applications
Proposes four research directions for real-world MAPF
Emphasizes importance of addressing real-world constraints
Abstract
Multi-agent path finding (MAPF) is well-studied in artificial intelligence, robotics, theoretical computer science and operations research. We discuss issues that arise when generalizing MAPF methods to real-world scenarios and four research directions that address them. We emphasize the importance of addressing these issues as opposed to developing faster methods for the standard formulation of the MAPF problem.
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Taxonomy
TopicsRobotic Path Planning Algorithms · Robotics and Sensor-Based Localization · Multimodal Machine Learning Applications