Hybrid Classical/RL Local Planner for Ground Robot Navigation

TL;DR

This paper introduces a hybrid local planner for ground robot navigation that combines a real-time exploration-based planner and a reinforcement learning-based planner, switching between them to optimize navigation performance.

Contribution

It proposes a simple meta-reasoning approach that switches between two different local planners to improve navigation efficiency and obstacle avoidance.

Findings

Achieved 26% reduction in navigation time.

Demonstrated superior performance over individual planners.

Validated approach on a live robot in various scenarios.

Abstract

Local planning is an optimization process within a mobile robot navigation stack that searches for the best velocity vector, given the robot and environment state. Depending on how the optimization criteria and constraints are defined, some planners may be better than others in specific situations. We consider two conceptually different planners. The first planner explores the velocity space in real-time and has superior path-tracking and motion smoothness performance. The second planner was trained using reinforcement learning methods to produce the best velocity based on its training $"$experience$"$. It is better at avoiding dynamic obstacles but at the expense of motion smoothness. We propose a simple yet effective meta-reasoning approach that takes advantage of both approaches by switching between planners based on the surroundings. We demonstrate the superiority of our hybrid planner, both qualitatively and quantitatively, over the individual planners on a live robot in different scenarios, achieving an improvement of 26% in the navigation time.