Research On Mobile Robot Indoor Positioning Algorithm

With the continuous development of smart home system, the research of indoor mobile robot localization method gradually become the study hotspot, also to the requirement of the mobile robot positioning accuracy. In the outdoor environment GPS technology performs good positioning accuracy, however, in the face of the complexity of the indoor environment and building block factors, GPS is limited by a lot. Therefore, many scholars begin to the research of mobile robot positioning method in indoor environment, Aiming at the problems of the current self-Localization algorithms for indoor mobile robot, such as the low positioning accuracy, increasing positioning error with time, the signal’s multipath effect and non-line-of-sight effect, the research carried out into the following two aspects.1) An algorithm using RTFL(RFID tag floor based localization) and maximum likelihood method was proposed. The new method is less affected by the environment and has higher localization accuracy. The initial estimation position of the robot and the boundary of the robot may exist are given by RTFL algorithm, which can shorten the search range and reduce the computational time of genetic algorithm effectively. A method that solves the maximum likelihood equations by the genetic algorithm was proposed, which improves the position precision of the robot obviously. The results of experiments show that the new system is less affected by environment, has a good robustness and can accomplish positioning effectively. Positioning accuracy of the new system can less than 0.5 m that can well meet the requirement of indoor mobile robot localization.2) Considering the time complexity of the algorithm, the delay of the robot may exist in the process of mobile data acquisition and calculation. A novel mobile robot self-localization method based on Monte Carlo Localization was proposed. Through analyzing the RTFL mobile robot self-localization system, the robot motion model was established and through the analysis of the mobile robot positioning system based on RSSI, the observation model was put forward. In the new positioning method, the particle culling strategy was proposed and the particle weight strategy was given based on orientation of the particles, thus greatly enhanced the efficiency of the new positioning system. Simulation results show that the new localization algorithm has greatly improved the positioning accuracy of the robot, and the position error can reach about 3cm in both the x direction and the y direction, while position error of the traditional localization algorithm in the x direction and in the y direction are in 6 cm, and the new localization algorithm has good robustness.3) The proposed methods can improve the positioning precision of the robot in the indoor environment, since the indoor mobile robot localization method based on genetic algorithm, has high positioning accuracy when the robot is in a stationary state, but when the robot is in a state of movement, the positioning accuracy of robots will be affected; Thus put forward the indoor mobile robot localization based on particle filter method can very good satisfy the positioning accuracy in the process of robot in motion..