Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint Generators

TL;DR

This paper presents a method to integrate Deep Reinforcement Learning-based obstacle avoidance with traditional global planners via waypoint generators, enhancing navigation safety, efficiency, and smoothness in dynamic environments.

Contribution

It introduces an intermediate planner concept that connects deep RL obstacle avoidance with conventional global planning using waypoint generators, enabling better system integration.

Findings

Improved safety in dynamic environments

Enhanced efficiency and path smoothness

Successful integration of deep RL with traditional navigation systems

Abstract

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.