Positioning And Navigation Of Mobile Robot Based On Information Fusion And Application Of Deep Sea Mining

Multi-sensor information fusion for positioning and navigation for mobile robot is one of the most challenging domains, especially for the robot operating under unknown complicate environments, for instance, the miner for sea-bed mining. It is a ROV (Remotely Operated Vehicle) which operates on the sea-bed, collects manganese nodules along a certain route and transports them to mining ship via lifting pipe system.The current main researches are focused on techniques for commonly used mobile robot. For the reasons of technical know-how and international competition, the research on positioning and navigation for ROV under deep sea-bed environments still remains on very low level, the uniform and integrated knowledge system has not been formed. By starting at the prospective basic work and means of multi-sensor fusion process, the research in this thesis aims at processing on data and information, providing theoretical and design methods for the applications of under water or sea-bed mobile robot. The innovative researches are as follows.Firstly, with respect to the characteristics and errors of different kinds of sensors, the errors of proprioceptive and exteroceptive sensors such as dead reckoning, Infrared/laser, GPS are analyzed on the platform of mobile robot MKâ…¡. An adaptive weighted fusion estimation is applied, which is on the condition of least mean square errors of GPS and the measures for each sensors, the optimum weighted factors is found to reach optimum estimation for fusion. By using this fusion algorithm, the precision positioning and navigation is achieved by fusing the GPS and odometry signals and the precision docking with an accuracy of less than mms is achieved by infrared and laser guidance docking. With respect to the positioning for ROV in deep-sea mining based on LBL acoustic system, an iterative algorithm model is proposed which is used to correct the acoustic speed. The simulation results show effectiveness of the model.Secondly, on the basis of deep-sea mining platform, the obstacle avoidance and positioning and navigation for ROV in unknown environments are studied. For considering the experiments in the deep sea environments is a little bit difficult to perform, the establishment of state space model and observation model of mobile robot, the building of fusion map and obstacle avoidance tragedy are studied on the platform of robot MKII, simulation analysis and experiments are performed, and then the above researches are applied on obstacle avoidance, self-localization and trajectory tracking for miner in deep sea.As for obstacle avoidance problem, since the sonar signal used for obstacle avoidance is very weak, the information fusion method using SOG(Sum of Gaussians)is introduced, and then the features are extracted in order to reduce the uncertainty of sensor information. Obstacle avoidance algorithm based on artificial potential field method is improved to let the robot skip from the local minimum and get to the goal smoothly. With respect to the positioning and navigation of ROV, The state space model of the crawler type miner was constructed similarly to the dead-reckoning line model of mobile robot. The measurement equation of the LBL system was presented according to the latency of the system. The fuzzy adaptive Kalman filter (FAKF) based on fuzzy logic control and corresponding control rules are applied in order to restrain the divergence and improve the robustness of the system. Since the kinematics model of crawler miner is strongly nonlinear, an improved SUKF algorithm is introduced to meet the nonlinear system. The position estimation and trajectory tracking are achieved based on LBL system.In the end, simultaneous localization and map building (SLAM problem) in unknown environment is studied. The concept of dynamic threshold is applied in the pre-process of data association information. The set of candidate observations are selected to reduce the computer complex and meet the real time requirement. During the match process nearest neighbor method based on dynamic threshold is adopted to suit the change of uncertainty in the real situation and to ensure the accuracy and real-time property for SLAM of mobile robot. On the basis of basic research mentioned above, considering the shortcomings of KFSLAM algorithm and nonlinear characteristics of the kinematics model, the Unscented KF method is introduced to improve the nonlinearity of the system. The position estimation of the robot and environment features are assessed at the same time. The particle filter (PF) based on sequential importance sampling algorithm is improved. The experimental and simulation results show the correctness of the algorithm.