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 collisionfree 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.