  Deep Reinforcement Learning has emerged as an efficient dynamic obstacle
avoidance method in highly dynamic environments. It has the potential to
replace overly conservative or inefficient navigation approaches. However, the
integration of Deep Reinforcement Learning into existing navigation systems is
still an open frontier due to the myopic nature of
Deep-Reinforcement-Learning-based navigation, which hinders its widespread
integration into current navigation systems. In this paper, we propose the
concept of an intermediate planner to interconnect novel
Deep-Reinforcement-Learning-based obstacle avoidance with conventional global
planning methods using waypoint generation. Therefore, we integrate different
waypoint generators into existing navigation systems and compare the joint
system against traditional ones. We found an increased performance in terms of
safety, efficiency and path smoothness especially in highly dynamic
environments.
