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Research on Self-localization of Indoor Mobile Robot

Author: ChenHuan
Tutor: ZuoHuaQing
School: South China University of Technology
Course: Applied Computer Technology
Keywords: Robot self-positioning Particle Filter Scan matching Hill-climbing algorithm
CLC: TP242
Type: Master's thesis
Year: 2010
Downloads: 157
Quote: 1
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Abstract


The extent and level of application of robotics research reflects the level of development of a country's level of industrial automation and has important defense strategic significance. Is the self-positioning of mobile robots rely on sensor carries its own estimate own specific position and orientation in the environment map, it is the basis for the mobile robot to perform other tasks. This paper studies a single mobile robot based on the odometer and laser sensors in the typical indoor environment the theory and implementation of self-positioning algorithm. First, describe indoor mobile robot self-localization problem, systematic introduction to Bayesian filtering theory and the Kalman filter, extended Kalman filter and particle filter implementation. Secondly, describes the track based on odometer projections positioning method and movement probabilistic model to study the scan matching localization method based on the laser sensor, the representation of the three environments map. Traditional iterative closest point scan matching algorithm requires a good pre-registration deficiencies of the initial solution, proposed the genetic iterative closest point scan matching algorithm using genetic algorithm to search for optimal scan matching solution to solve any scan matching algorithm alignment problems and improve the accuracy of positioning of the robot. Finally, the focus of research and particle filter-based indoor mobile robot self-localization algorithm. Of the important factors that affect the performance of the algorithm, such as the proposed distribution, weight calculation method, re-sampling method, the number of particles, such as a detailed discussion and analysis; proposed the global random particle auxiliary filter method to solve the problem of robot kidnapping and global positioning failure recovery ; climbing algorithm to optimize the global distribution of particles, and reduce the number of particles and computing time, improve the efficiency of the global positioning; cause positioning errors for unknown obstacles in the dynamic environment, research and analysis to study the mode of the laser scan data in the scenario, the unknown obstacle detection and filtering algorithm to solve the problem of robot localization in a dynamic environment; From a practical point of view, analysis of the running time of each step of the algorithm proposed a step-by-step sampling method based on time constraints; final integration various modifications of the proposed method, the algorithm is given the overall process framework. Through simulation and real robot experiment to verify the effectiveness and robustness of the improved positioning algorithm.

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > Robotics > Robot
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