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