Localization and controlling the mobile robot by sensory data fusion

Localization ­of a mobile robot with any structure, work space and task is one of the most fundamental issues in the field of robotics and the prerequisite for moving any mobile robot that has always been a challenge for researchers. In this paper, the Dempster-Shafer and Kalman filter methods are used as the two main tools for the integration and processing of sensor data in robot localization to achieve the best estimate of positioning according to the unsteady environmental conditions and a framework for GPS and IMU sensor data fusion through the Dempster-Shafer method. Also, by providing a new method, the initial weighing on each of these GPS sensors and wheel encoders is done based on the reliability of each one. The methods were compared with the simulation model and the best method was chosen in each situation. In addition to obtaining the geometric equations governing the robot, a PID controller was used for the kinematic control of the robot and implemented in the MATLAB Simulink. Also, using the two MAD and MSE criteria, the localization error was compared in both K.F. and D.S. methods. In normal Gaussian noise, the K.F. with a mean error of 2.59% performed better than the D.S. method with a 3.12% error. However, in terms of non-Gaussian noise exposure, which we are faced with in real condition, K.F. information was associated with a moderate error of 1.4, while the D.S. behavior in the face of these conditions was not significantly changed