Research On Navigation And Environment Modeling Of Mobile Robot

Researches on the autonomous navigation, environmental modeling and kinematic control of mobile robot in feature based indoor environments are presented in this dissertation.Firstly, the dissertation analyzes various infections on different sensors. It presents a characterization study of the LMS 200 laser range finder and shows the advantages of it in mobile robot research. Uncertainty model in range measurement is built based on the referenced experiment results. Then it points out the limitation in dead-reckoning and gives out the state covariance matrix. In addition, the camera system is calibrated and compensated against distortion successfully.Secondly,feature extracting, merging and fusion are decisive factors for mobile robot localization and map building. All of the localization approaches in this dissertation are feature based instead of directly using the raw data from on-board sensors. Due to the variant sensor modeling, ascertaining the formula of one line by two points, fitting the formula of one line by umpty points with least square algorithm for laser range finder to extract certain 2-D horizontal environmental features, and extracting vertical edges for CCD camera, non-maxima suppression algorithm is used. After the further matching between horizontal environmental features and vertical edges, the robot can use the high-level feature as natural landmark in localization and navigation.Thirdly, the representation of environment is crucial for localization and map building. In this dissertation,research on mobile robot localization approach with priori map can be classified as EKF-based algorithm, Integrated probability localization algorithm and hybrid metric-topological algorithm. In the method of pose tracking, as for long distance movement, accumulated error, brought with the use of dead-reckoning method only, must be eliminated with the help of external sensor information. Matching the extracted feature, obtained from external sensors such as laser range finder and CCD camera, with environment model. And then update the pose estimation with EKF algorithm. Global localization is the most important behaviors for autonomous navigation of robot in a foregone environment, for which robustness and precision is a necessity. To enhance the performance of Markov and Kalman filter for application, Markov-EKF is proposed for robust global localization and precise pose tracking. Experiment validates the performance of Markov-EKF method with localization error comparison with Markov method given. Localization of mobile robot based on hybrid metric-topological map carries out metric map based localization in local map, and topological map based locali- zation in global map. In this way, robot can still accomplish the initialization of EKF algorithm again based on bound advanced features with the topology nodes, even if pose tracking failed because of accident like collision. This can make sure the continuity of the whole localization process, and can realize precise localization in some specific task.Fourthly, environmental modeling online-SLAM(Simultaneous Localization and Mapping) is another important problem for mobile robot. A hybrid hierarchical mapping method is proposed which integrates topological and feature based method to extend to large scale and cyclic environments, thus reducing the computation complexity and improving the precision of environment model.Fifthly, globally trajectory tracking control of mobile robot is studied. For architecture of Pioneer 2/DX, its kinematics models and tracking models is deduced.Based on fuzzy control method, it makes the mobile robot realize robust control of trajectory tracking. And employing Particle swarm optimization algorithm optimizes the integral-fuzzy controller thereafter. And then static errors of mobile robot system are eliminated. Consequentially, the mobile robot not only possesses better dynamic characteristics, but also acquires satisfactory effectiveness of tracking.Finally, the obtained results are summarized and future work is addressed.