  Navigation of mobile robots within crowded environments is an essential task
in various use cases, such as delivery, health care, or logistics. Deep
Reinforcement Learning (DRL) emerged as an alternative method to replace overly
conservative approaches and promises more efficient and flexible navigation.
However, Deep Reinforcement Learning is limited to local navigation due to its
myopic nature. Previous research works proposed various ways to combine Deep
Reinforcement Learning with conventional methods but a common problem is the
complexity of highly dynamic environments due to the unpredictability of humans
and other objects within the environment. In this paper, we propose a
hierarchical waypoint generator, which considers moving obstacles and thus
generates safer and more robust waypoints for Deep-Reinforcement-Learning-based
local planners. Therefore, we utilize Delaunay Triangulation to encode
obstacles and incorporate an extended hybrid A-Star approach to efficiently
search for an optimal solution in the time-state space. We compared our
waypoint generator against two baseline approaches and outperform them in terms
of safety, efficiency, and robustness.
