Multi Agent Path Finding with Awareness for Spatially Extended Agents

TL;DR

This paper introduces XCBS-A and XCBS-LA algorithms for conflict-free path planning of spatially extended agents, improving efficiency, completeness, and optimality in multi-agent navigation on road networks.

Contribution

It presents the XCBS-A algorithm with awareness for multi-agent pathfinding of extended agents, and proposes XCBS-LA for guaranteed optimal solutions, advancing existing methods.

Findings

XCBS-A is complete and empirically effective.

XCBS-LA guarantees optimal and complete solutions.

The algorithms perform well across varied road and agent scenarios.

Abstract

Path finding problems involve identification of a plan for conflict free movement of agents over a common road network. Most approaches to this problem handle the agents as point objects, wherein the size of the agent is significantly smaller than the road on which it travels. In this paper, we consider spatially extended agents which have a size comparable to the length of the road on which they travel. An optimal multi agent path finding approach for spatially-extended agents was proposed in the eXtended Conflict Based Search (XCBS) algorithm. As XCBS resolves only a pair of conflicts at a time, it results in deeper search trees in case of cascading or multiple (more than two agent) conflicts at a given location. This issue is addressed in eXtended Conflict Based Search with Awareness (XCBS-A) in which an agent uses awareness of other agents' plans to make its own plan. In this paper, we explore XCBS-A in greater detail, we theoretically prove its completeness and empirically demonstrate its performance with other algorithms in terms of variances in road characteristics, agent characteristics and plan characteristics. We demonstrate the distributive nature of the algorithm by evaluating its performance when distributed over multiple machines. XCBS-A generates a huge search space impacting its efficiency in terms of memory; to address this we propose an approach for memory-efficiency and empirically demonstrate the performance of the algorithm. The nature of XCBS-A is such that it may lead to suboptimal solutions, hence the final contribution of this paper is an enhanced approach, XCBS-Local Awareness (XCBS-LA) which we prove will be optimal and complete.