Planning the path with Reinforcement Learning: Optimal Robot Motion Planning in RoboCup Small Size League Environments

TL;DR

This paper explores the use of Reinforcement Learning to improve robot motion planning in RoboCup SSL environments, demonstrating significant performance gains and dynamic obstacle avoidance capabilities.

Contribution

It introduces a RL-based approach for robot path planning in SSL, showing improved efficiency and obstacle avoidance over traditional methods.

Findings

60% time reduction in obstacle-free environments

Effective dynamic obstacle avoidance

Significant performance improvements with ablation studies

Abstract

This work investigates the potential of Reinforcement Learning (RL) to tackle robot motion planning challenges in the dynamic RoboCup Small Size League (SSL). Using a heuristic control approach, we evaluate RL's effectiveness in obstacle-free and single-obstacle path-planning environments. Ablation studies reveal significant performance improvements. Our method achieved a 60% time gain in obstacle-free environments compared to baseline algorithms. Additionally, our findings demonstrated dynamic obstacle avoidance capabilities, adeptly navigating around moving blocks. These findings highlight the potential of RL to enhance robot motion planning in the challenging and unpredictable SSL environment.