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

Mateus G. Machado, João G. Melo, Cleber Zanchettin, Pedro H. M. Braga, Pedro V. Cunha +2 more
4/23/2024
cs.ROcs.AIcs.LG

No official code found. Best implementation: goncamateus/Planning-the-path-with-rl (4 stars, MIT, last commit 2025-06-12).

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.

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Code Implementations(1)

goncamateus/Planning-the-path-with-rlCommunity100%
MIT

Study of Reinforcement Learning in the tasks of Path and trajectory planning in the RoboCup Small Size League environment

43Shell, PythonNov 21, 20231 years agoMIT

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Cite this paper

@article{machado2024planning,
  title  = {Planning the path with Reinforcement Learning: Optimal Robot Motion Planning in RoboCup Small Size League Environments},
  author = {Mateus G. Machado and João G. Melo and Cleber Zanchettin and Pedro H. M. Braga and Pedro V. Cunha and Edna N. S. Barros and Hansenclever F. Bassani},
  year   = {2024},
  eprint = {2404.15410},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.15410v1}
}

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