Before revision:  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. The issues are: 1. DWA not utilizing the time evolution of the environment while choosing velocities from the dynamically feasible velocity set leading to sub-optimal dynamic collision avoidance behaviors, and 2. DRL-based navigation policies computing velocities that often violate the dynamics constraints such as the non-holonomic and acceleration constraints of the robot. Our DRL-based method generates velocities that are dynamically feasible while accounting for the motion of the obstacles in the environment. This is done by embedding the changes in the environment' s state in a novel observation space and a reward function formulation that reinforces spatially aware obstacleavoidance maneuvers .
After revision:  Our method addresses two primary issues associated with the Dynamic Window Approach (DWA) and DRL-based navigation methods that can handle moving obstacles and pedestrians well. Our formulation achieves these goals by embedding the changes in the environment' s state in a novel observation space and a reward function formulation that reinforces spatially aware obstacleavoidance maneuvers .