Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint Generators
This work addresses the integration challenge for robotics and autonomous systems, but it is incremental as it builds on existing DRL and global planning methods.
The paper tackled the integration of Deep Reinforcement Learning (DRL) for obstacle avoidance into existing navigation systems by proposing an intermediate planner with waypoint generators, resulting in increased performance in safety, efficiency, and path smoothness in highly dynamic environments.
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.