All-in-One: A DRL-based Control Switch Combining State-of-the-art Navigation Planners
This addresses navigation challenges for mobile robots in dynamic environments, but it is incremental as it combines existing planners rather than introducing a fundamentally new method.
The paper tackled the problem of autonomous robot navigation by proposing a DRL-based control switch that selects between different planning paradigms based on sensor data, resulting in improved navigation performance, especially in highly dynamic scenarios.
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.