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.
Abstract (translated)
这项工作研究了强化学习（RL）解决机器人运动规划挑战在动态机器人杯小型联赛（SSL）中的潜在能力。我们使用一种启发式控制方法来评估RL在无障碍和单障碍路径规划环境中的效果。消融研究揭示了显著的性能提升。与基线算法相比，我们的方法在无障碍环境中取得了60%的性能提升。此外，我们的研究结果表明，RL具有动态避障能力，能够熟练地围绕移动障碍物进行导航。这些发现突出了RL在具有挑战性和不可预测性的SSL环境中增强机器人运动规划的潜力。
URL
https://arxiv.org/abs/2404.15410
