An enhanced path planning of fast mobile robot based on data fusion of image sensor and GPS

This paper presents a path planning algorithm for a fast mobile robot based on extended Kalman filter (EKF) by fusing the satellite navigation and the vision system in the outdoor environment. The suggested approach offers several improvements that result in smoother trajectories and greater reliability. The noisy location information of a robot is enhanced by using the vision system which contain abundant information with high accuracy but is subject to noise. This research consists of a motion segmentation stage which gets motion information of moving objects form motion model, and a motion estimation stage which estimates the position and the motion of moving object using EKF. EKF based approach is served as the de-facto approach to SLAM with shortcomings of quadratic complexity and sensitivity to failures in data association. The simulation results show a greater reliability for fast mobile robot navigation under outdoor environment.