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

arXiv 2404.15410 ↗First posted 23 Apr 2024cs.RO cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 29 references that are in the map. First posted on arXiv 23 Apr 2024.

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References · 13

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  12. 2021An Analysis of Frame-skipping in Reinforcement Learningcited by 17
1 more
  1. 2021rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot Soccercited by 4

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