LAC-Nav: Collision-Free Mutiagent Navigation Based on The Local Action Cells

TL;DR

LAC-Nav introduces local action cells for decentralized multiagent collision-free navigation, utilizing real-time updates and adaptive learning to improve safety and efficiency in various scenarios.

Contribution

The paper presents a novel local action cell concept combined with adaptive learning for decentralized multiagent collision avoidance.

Findings

Effective in three common scenarios

Outperforms several existing strategies

Enhances safety and efficiency

Abstract

Collision avoidance is one of the most primary requirement in the decentralized multiagent navigations: while the agents are moving towards their own targets, attentions should be paid to avoid the collisions with the others. In this paper, we introduce the concept of local action cell, which provides for each agent a set of velocities that are safe to perform. Based on the realtime updated local action cells, we propose the LAC-Nav approach to navigate the agent with the properly selected velocity; and furthermore, we coupled the local action cell with an adaptive learning framework, in which the effect of selections are evaluated and used as the references for making decisions in the following updates. Through the experiments for three commonly considered scenarios, we demonstrated the efficiency of the proposed approaches, with the comparison to several widely studied strategies.