Mobile robot GPS/DR integrated navigation positioning technique research

Abstract

GPS is widely used for global positioning system. But GPS signal is easily interrupted when it is used alone. DR (dead reckoning) can calculate the position of mobile robots by using direction and speed sensors. However, DR system error can accumulate over time due to the error of electronic compass and odometer sensors. So DR system can't be used separately for a long time. The integrated navigation system combined GPS with DR will effectively integrated advantages of these two systems, higher positioning precision and reliability. In this paper Kalman filter model for GPS/DR integrated navigation system is set up to filter the GPS and DR data. And then the outputs of Kalman filter are inputted to a BP neural network for training. BP neural network is employed to predict next sampling time GPS output and a new Kalman filter based data fusion method is proposed to do the navigation information fusion with encoders and compass system. Simulation is done to validate the proposed fusion method. The simulation result shows the potential of this fusion method for outside used mobile robot navigation. Finally experiments are done to validate the proposed fusion method.