  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.
