Proceedings of the 6th International Conference on Applied Engineering, ICAE 2023, 7 November 2023, Batam, Riau islands, Indonesia

Research Article

Implementation of Rapidly-Exploring Random Tree (RRT) for Path Planning and Stanley Control Method for Path Tracking on Wheeled Soccer Robots

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  • @INPROCEEDINGS{10.4108/eai.7-11-2023.2342945,
        author={Hendawan  Soebhakti and Budiana  Budiana and Fahri  Rahmat and Masdika  Aliman and Nyoman Krisna Prebawa},
        title={Implementation of Rapidly-Exploring Random Tree (RRT) for Path Planning and Stanley Control Method for Path Tracking on Wheeled Soccer Robots},
        proceedings={Proceedings of the 6th International Conference on Applied Engineering, ICAE 2023, 7 November 2023, Batam, Riau islands, Indonesia},
        publisher={EAI},
        proceedings_a={ICAE},
        year={2024},
        month={1},
        keywords={rrt stanley control robot soccer},
        doi={10.4108/eai.7-11-2023.2342945}
    }
    
  • Hendawan Soebhakti
    Budiana Budiana
    Fahri Rahmat
    Masdika Aliman
    Nyoman Krisna Prebawa
    Year: 2024
    Implementation of Rapidly-Exploring Random Tree (RRT) for Path Planning and Stanley Control Method for Path Tracking on Wheeled Soccer Robots
    ICAE
    EAI
    DOI: 10.4108/eai.7-11-2023.2342945
Hendawan Soebhakti1,*, Budiana Budiana1, Fahri Rahmat1, Masdika Aliman1, Nyoman Krisna Prebawa1
  • 1: Politeknik Negeri Batam
*Contact email: hendawan@polibatam.ac.id

Abstract

A wheeled soccer robot, designed for autonomous play, faces challenges in motion planning due to dynamic player interactions. This involves generating optimal paths for goal-oriented movements, utilizing techniques like Rapidly-exploring Random Trees (RRTs) for path planning and Stanley control for path tracking. Results at 80 cm/s show a Root Mean Square Error (RMSE) of 14.02 cm (x-axis) and 12.9 cm (y-axis). In the third test, RMSE is 6.04 cm (x-axis) and 14.16 cm (y-axis), with a 7.12-second travel time. The motion planning system, employing RRTs and Stanley Control, produces collision-free trajectories, tracked effectively in real-time. Obstacle positioning impacts travel time but doesn't impede trajectory selection. The system adeptly generates and tracks optimal paths for wheeled soccer robots.