  We present a novel Deep Reinforcement Learning (DRL) based policy to compute
dynamically feasible and spatially aware velocities for a robot navigating
among mobile obstacles. Our approach combines the benefits of the Dynamic
Window Approach (DWA) in terms of satisfying the robot's dynamics constraints
with state-of-the-art DRL-based navigation methods that can handle moving
obstacles and pedestrians well. Our formulation achieves these goals by
embedding the environmental obstacles' motions in a novel low-dimensional
observation space. It also uses a novel reward function to positively reinforce
velocities that move the robot away from the obstacle's heading direction
leading to significantly lower number of collisions. We evaluate our method in
realistic 3-D simulated environments and on a real differential drive robot in
challenging dense indoor scenarios with several walking pedestrians. We compare
our method with state-of-the-art collision avoidance methods and observe
significant improvements in terms of success rate (up to 33\% increase), number
of dynamics constraint violations (up to 61\% decrease), and smoothness. We
also conduct ablation studies to highlight the advantages of our observation
space formulation, and reward structure.
