The Combination Of Gps / Dr-based Mobile Robot Positioning Technology

Global Position System (GPS) can provide 24-hour, real-time absolute localization. Its error is never accumulated over time, but the GPS satellite signal is vulnerable to external environmental disturbance, such as occlusions, multipath effect etc. It can make the satellite signal becomes poor or even interrupted, which cannot be completed positioning. So to make a continuous and reliable positioning information needs to have other auxiliary system. Dead Reckoning (DR) is an independent positioning technology based on relative position correction, with the advantages of short time and higher precision. Its validity will not be subjected to the influence of external factors, but the relative position can be determined only, and its error will be accumulated with calculating unceasingly. Thus, GPS has long time absolute localization stability but DR has short time relative positioning stability. They have very strong complementary relationship.This paper mainly studies the integrated localization technology based on GPS/DR of the mobile robot, it makes the mobile robot have the good positioning capability in the complex environment. Firstly it studies the basic positioning principle and error sources of GPS system and DR system, then discusses the strengths and weaknesses of GPS or DR alone positioning by experiment, and identifies the superiority of the GPS/DR integrated positioning. Secondly, the paper mainly studies multi-sensor information fusion algorithms, including kalman filter (KF) algorithm in linear system, extended kalman filter (EKF) algorithm and scaled unscented kalman filter (SUKF) algorithm in nonlinear system. The EKF algorithm ignores higher-order item to achieve linearization by Taylor expansion of nonlinear function for first-order truncation, but this process also increases error. The SUKF algorithm approximates nonlinear density function by getting a certain number of sampling points with statistical properties of state variable to ensure that the precision achieves at least of second order after the state variable transferred. Thus, the model is more accurate. Finally, the paper establishes the mathematical model of GPS/DR combination positioning system. Aiming at the features of the model state equation and observed equation, and combining with research of SUKF and KF algorithm theories, the paper improves the SUKF algorithm.In order to validate the performance of the algorithm, experimental platform is built, experimental data is collected by mobile robot, the EKF,SUKF,improved SUKF algorithms are implemented by VC programming, the collected experimental data is handled by information fusion technology. Experimental results show that the precision of information fusion by using SUKF is superior than using EKF; the improved SUKF algorithm has the same filtering precision as the SUKF algorithm, but it simplifies the algorithm process, reduces the computation time and improves the efficiency of the algorithm in a certain extent.