Before revision: We present a novel Deep Reinforcement Learning (DRL) based policy for mobile robot navigation in dynamic environments that computes dynamically feasible and spatially aware robot velocities . Our method addresses two primary issues associated with the Dynamic Window Approach (DWA) and DRL-based navigation policies and solves them by using the benefits of one method to fix the issues of the other.
After revision: We present a novel Deep Reinforcement Learning (DRL) based policy for mobile robot navigation in dynamic environments that computes dynamically feasible and spatially aware velocities for a robot navigating among mobile obstacles. Our approach combines the benefits of the Dynamic Window Approach (DWA) and DRL-based navigation policies and solves them by using the benefits of one method to fix the issues of the other.