The Research On Localization Technology For Intelligent Mobile Robot

With the development of sensor, computer and artificial intelligence, the ground intelligent mobile robot with idea, apperception and action capability has been used widely in the field of military affairs, civil and scientific research. Its development has imposing on the defense, society, and academy, and becomes the tactic research object of high technology of all countries. In order to finish tasks successfully, one of basic conditions for the ground intelligent mobile robot is the autonomous navigation in its environment and the autonomous navigation need to locate itself. This dissertation is focused on localization technology for the intelligent mobile robot, which makes the ground intelligent mobile robot have a better autonomous localization capability in all kinds of complex environment. The main content of this dissertation include the following aspects:The main function of the localization system for the ground intelligent mobile robot is to ascertain its referenced localization on the surface of earth. The coordinate system is the mathematics and physics foundation to describe the motion of ground intelligent mobile robot, deal with observation data and show its localization. The usual coordinate systems in ground intelligent mobile robot localization are discussed at first. The WGS-84 spacial orthogonal coordinate system, WGS-84 geodetic coordinate system, Gauss plane orthogonal coordinate system and robot plane orthogonal coordinate system can be translated with each other.The GPS carrier wave phasic observation function is introduced and based on which the double differential GPS coordinate computation model is deduced. In order to ensure differential GPS localizition precision in a large zone, the algorithm of differential GPS is researched based on virtual referene station (VRS). The single and double differential observation data on VRS is deduced detailedly.Aiming at the integrated localization issue based on differential GPS/DR, an algorithm based on scale unscented transformation and extended kalman filter (SUT-EKF) is presented. For the characteristic of nonlinear state equation and linear measurement equation in the integrated localization system based on differential GPS/DR, the robot location can be predicted by SUT and can be updated with new observations by EKF. The algorithm doesn't compute the Jacobian matrix, it can decrease effectively the error of nonlinear system brought by the linearization.An algorithm for simultaneous localization and mapping (SLAM) based on scale unscented transformation and iterative extended kalman filter (SUT-IEKF) is presented. Data association plays an important role in the precision of robot localization, especially, the algorithm of SLAM based on EKF is very frail to the wrong data association. A data association method based on multi algorithm matching is proposed. It uses equal weight particles to denote the joint probability distribution of the robot and feature map. Each of particles applies different data association algorithm and gets different data association set during SLAM, the intersecting set of all sets is taken as the objective set.An algorithm for SLAM based on combined filter is researched and use the statistic theory to evaluate the consistency. It decompose the joint posterior probability distribution into robot path part and feature map part through the particle filter, which make the filter become low dimensional filter and can improve the computational efficiency. The constrained unscented kalman filter (CUKF) make the proposal distribution much closer to the posterior probability distribution with new observations and the robot pose can be estimated accurately. The extended kalman filter (EKF) is used to update the feature map localization.The method about distributed multi robot cooperative localization is discussed. The accurate robot localization can be acquired using distributed unscented kalman filter (UKF) fused with other robots'relative observation information. An algorithm for cooperative simultaneous localization and mapping (C-SLAM) based on distributed unscent particle filter (UPF) is described. Each of robots runs an UPF. When one member of the team may not observe the landmarks, it can estimate its pose through dead-reckoning, but the precision is too limited. In order to improve the precision, a novel approach is to let the robot observe other robot with better landmarks observations and get the relative observations. Let they keep continuous relative observations and continuously exchange relative information. The robot can construct Virtual Observations (VO) with the relative observations and perform C-SLAM based on VO, which can improve effectively the localization precision.Finally, we summarize the general work of this dissertation and give a short outlook on possible future research.