  Path planning for mobile robots in large dynamic environments is a
challenging problem, as the robots are required to efficiently reach their
given goals while simultaneously avoiding potential conflicts with other robots
or dynamic objects. In the presence of dynamic obstacles, traditional solutions
usually employ re-planning strategies, which re-call a planning algorithm to
search for an alternative path whenever the robot encounters a conflict.
However, such re-planning strategies often cause unnecessary detours. To
address this issue, we propose a learning-based technique that exploits
environmental spatio-temporal information. Different from existing
learning-based methods, we introduce a globally guided reinforcement learning
approach (G2RL), which incorporates a novel reward structure that generalizes
to arbitrary environments. We apply G2RL to solve the multi-robot path planning
problem in a fully distributed reactive manner. We evaluate our method across
different map types, obstacle densities, and the number of robots. Experimental
results show that G2RL generalizes well, outperforming existing distributed
methods, and performing very similarly to fully centralized state-of-the-art
benchmarks.
