  Autonomous navigation of mobile robots is an essential aspect in use cases
such as delivery, assistance or logistics. Although traditional planning
methods are well integrated into existing navigation systems, they struggle in
highly dynamic environments. On the other hand,
Deep-Reinforcement-Learning-based methods show superior performance in dynamic
obstacle avoidance but are not suitable for long-range navigation and struggle
with local minima. In this paper, we propose a
Deep-Reinforcement-Learning-based control switch, which has the ability to
select between different planning paradigms based solely on sensor data
observations. Therefore, we develop an interface to efficiently operate
multiple model-based, as well as learning-based local planners and integrate a
variety of state-of-the-art planners to be selected by the control switch.
Subsequently, we evaluate our approach against each planner individually and
found improvements in navigation performance especially for highly dynamic
scenarios. Our planner was able to prefer learning-based approaches in
situations with a high number of obstacles while relying on the traditional
model-based planners in long corridors or empty spaces.
