Collision Detection for Agents in Multi-Agent Pathfinding

TL;DR

This paper reviews collision detection methods in multi-agent pathfinding, emphasizing approaches that balance computational efficiency with high accuracy to prevent false detections and improve practical performance.

Contribution

It provides a high-level overview of major collision detection categories and methods that enhance efficiency and accuracy in multi-agent pathfinding.

Findings

Categorizes collision detection approaches in MAPF.

Highlights methods balancing efficiency and accuracy.

Discusses anticipatory collision avoidance techniques.

Abstract

Recent work on the multi-agent pathfinding problem (MAPF) has begun to study agents with motion that is more complex, for example, with non-unit action durations and kinematic constraints. An important aspect of MAPF is collision detection. Many collision detection approaches exist, but often suffer from issues such as high computational cost or causing false negative or false positive detections. In practice, these issues can result in problems that range from inefficiency and annoyance to catastrophic. The main contribution of this technical report is to provide a high-level overview of major categories of collision detection, along with methods of collision detection and anticipatory collision avoidance for agents that are both computationally efficient and highly accurate.