DeepMNavigate: Deep Reinforced Multi-Robot Navigation Unifying Local Global Collision Avoidance [article]

Qingyang Tan, Tingxiang Fan, Jia Pan, Dinesh Manocha
2020 arXiv   pre-print
We present a novel algorithm (DeepMNavigate) for global multi-agent navigation in dense scenarios using deep reinforcement learning (DRL). Our approach uses local and global information for each robot from motion information maps. We use a three-layer CNN that takes these maps as input to generate a suitable action to drive each robot to its goal position. Our approach is general, learns an optimal policy using a multi-scenario, multi-state training algorithm, and can directly handle raw sensor
more » ... measurements for local observations. We demonstrate the performance on dense, complex benchmarks with narrow passages and environments with tens of agents. We highlight the algorithm's benefits over prior learning methods and geometric decentralized algorithms in complex scenarios.
arXiv:1910.09441v5 fatcat:yf5y45lcxze33h54isuvpvspom