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

arXiv:2404.15410v1 Announce Type: cross
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 …

abstract arxiv challenges control cs.ai cs.lg cs.ro dynamic environments free motion planning path planning reinforcement reinforcement learning robot small ssl type work