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Collision avoidance is one of the most primary requirement in thedecentralized multiagent navigations: while the agents are moving towards theirown targets, attentions should be paid to avoid the collisions with the others.In this paper, we introduce the concept of local action cell, which providesfor each agent a set of velocities that are safe to perform. Based on therealtime updated local action cells, we propose the LAC-Nav approach tonavigate the agent with the properly selected velocity; and furthermore, wecoupled the local action cell with an adaptive learning framework, in which theeffect of selections are evaluated and used as the references for makingdecisions in the following updates. Through the experiments for three commonlyconsidered scenarios, we demonstrated the efficiency of the proposedapproaches, with the comparison to several widely studied strategies.

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