All-in-One: A DRL-based Control Switch Combining State-of-the-art Navigation Planners

TL;DR

This paper introduces a DRL-based control switch that intelligently selects between traditional and learning-based navigation planners based on sensor data, improving robot navigation in dynamic environments.

Contribution

It develops an interface to integrate multiple planners and a control switch that chooses the best planner dynamically, enhancing navigation performance.

Findings

Improved navigation in highly dynamic scenarios.

Effective switching between planners based on environment.

Enhanced performance over individual planners.

Abstract

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.