Planning the path with Reinforcement Learning: Optimal Robot Motion Planning in RoboCup Small Size League Environments
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.
Code (1)
Tasks
Motion PlanningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
A Task-Efficient Reinforcement Learning Task-Motion Planner for Safe Human-Robot Cooperation
In a Human-Robot Cooperation (HRC) environment, safety and efficiency are the two core properties to evaluate robot performance. However, safety mechanisms usually hinder task efficiency since human intervention will cau…
Reinforcement LearningMotion PlanningA review of motion planning algorithms for intelligent robotics
We investigate and analyze principles of typical motion planning algorithms. These include traditional planning algorithms, supervised learning, optimal value reinforcement learning, policy gradient reinforcement learnin…
Motion PlanningQ-Learningreinforcement-learningReinforcement Learning+1Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as Observation
Industrial robots are widely used in various manufacturing environments due to their efficiency in doing repetitive tasks such as assembly or welding. A common problem for these applications is to reach a destination wit…
Deep Reinforcement LearningIndustrial RobotsMotion Planningreinforcement-learning+2Computational Tradeoff in Minimum Obstacle Displacement Planning for Robot Navigation
In this paper, we look into the minimum obstacle displacement (MOD) planning problem from a mobile robot motion planning perspective. This problem finds an optimal path to goal by displacing movable obstacles when no pat…
Motion PlanningRobot NavigationRobot Navigation with Reinforcement Learned Path Generation and Fine-Tuned Motion Control
In this paper, we propose a novel reinforcement learning (RL) based path generation (RL-PG) approach for mobile robot navigation without a prior exploration of an unknown environment. Multiple predictive path points are …
Reinforcement Learning (RL)Robot Navigation