Distributed Multi-agent Navigation Based on Reciprocal Collision Avoidance and Locally Confined Multi-agent Path Finding

TL;DR

This paper enhances multi-agent navigation by integrating collision avoidance with local MAPF solvers to effectively resolve deadlocks, significantly improving success rates in confined spaces.

Contribution

It introduces a novel approach combining ORCA-like collision avoidance with local MAPF solvers to address deadlocks in decentralized multi-agent navigation.

Findings

Success rate increased from 15% to 99% in certain scenarios.

Integration of MAPF solvers effectively resolves deadlocks.

The approach scales well to large numbers of agents.

Abstract

Avoiding collisions is the core problem in multi-agent navigation. In decentralized settings, when agents have limited communication and sensory capabilities, collisions are typically avoided in a reactive fashion, relying on local observations/communications. Prominent collision avoidance techniques, e.g. ORCA, are computationally efficient and scale well to a large number of agents. However, in numerous scenarios, involving navigation through the tight passages or confined spaces, deadlocks are likely to occur due to the egoistic behaviour of the agents and as a result, the latter can not achieve their goals. To this end, we suggest an application of the locally confined multi-agent path finding (MAPF) solvers that coordinate sub-groups of the agents that appear to be in a deadlock (to detect the latter we suggest a simple, yet efficient ad-hoc routine). We present a way to build a grid-based MAPF instance, typically required by modern MAPF solvers. We evaluate two of them in our experiments, i.e. Push and Rotate and a bounded-suboptimal version of Conflict Based Search (ECBS), and show that their inclusion into the navigation pipeline significantly increases the success rate, from 15% to 99% in certain cases.