Collision Detection for Agents in Multi-Agent Pathfinding
This work addresses collision detection problems for researchers and practitioners in multi-agent systems, but it appears incremental as it offers a high-level overview rather than a novel solution.
The paper tackles the challenge of collision detection in multi-agent pathfinding with complex agent motions, aiming to address issues like high computational cost and inaccuracies. It provides an overview of methods that are computationally efficient and highly accurate for collision detection and avoidance.
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.