Study Of Trajectory Tracking For A Deep-Sea Mining Robot Operating In A Complex Environment

Among deep-sea mineral resources, manganese nodules (include useful metals such as manganese, copper and nickel) have been the most important targets of ocean exploration because of increasingly shortage of mineral resources and huge commercial benefits. In the deep ocean mining system, a tracked vehicle is normally used to collect manganese nodules on the seabed. The vehicle is equipped with a pickup unit, crusher, hydraulic system, electronic box, flexible pipe, etc. It is designed to move on deep seabed autonomously, with high localization precision, so as to ensure high efficiency operation.This thesis describes the development of a mining vehicle model and the associated navigation system for a skid-steered tracked vehicle that is expected to operate on the deepsea floor. The system is to navigate accurately and reliably in an unstrucutered environment, which is a difficult task and requires accurate localisation and robust trajectory control. The thesis made five main contributions towards accurate and reliable navigation and control of tracked mining vehicles as follows:1) The thesis presents a novel approach to soil-track interaction modelling by incorporating track slips, the robot slip angle and track forces. The equations developed characterise the relationship between the forces acting on the vehicle, the robot parameters and key soil properties. The soil-track interaction equations are then used to develop a comprehensive motion model of a tracked robot. Incorporating kinematic and dynamic equations of robot motion with the soil equations allows robust and reliable estimation of the robot's position using an extended Kalman filter.2) Since the long base line (LBL) based sonar localization system of seabed mining vehicles is seriously affected by the noise in a working environment, and the accuracy of dead reckoning (DR) is seriously affected by vehicle slippage, the thesis introduces a kinematic model of a tracked mining vehicle in presence of sliding parameters, and describes its process and measurement noises based on experiment data collected from a lake. The innovation sequence is deployed to achieve the adaptive statistics features of both process and measurement noises. Taking into account the influence of measurement data delay, the Kalman filter fuses LBL and DR data to obtain the localization estimate of the seabed mining vehicle. Simulation results prove that the adaptive Kalman filter can deal with the changing statistics features of process and measurement noise very well, and has better localization estimation of the seabed mining vehicle than a normal Kalman filter.3) Since there are some defects when the extended Kalman filter (EKF) is applied in the nonlinear state estimation, unscented Kalman filter (UKF) is introduced in an integrated navigation system (LBL/DR) for the localization of a deep-sea mining vehicle. Compared with the EKF, the UKF not only improves the location accuracy, but also avoids the calculation burden of Jacobian matrices. This data fusion algorithm is easy to implement, and meets the requirements of low cost and high precision. The simulation result shows that the UKF method is more accurate and reliable than the EKF method for deep-sea mining vehicle navigation.4) This thesis introduces a kinematic model of a deep-sea mining vehicle in presence of sliding parameters. The model describes both the noise features of sliding parameters and the deep-sea condition features. To handle sliding parameters noises, a recursive algorithm is adopted to minimize difference between the filter-computed and the actual innovation covariance, which is a novel integrated navigation method based on unscented Kalman filters (UKF). Taking into account the influence of measurement data delay, UKF fuses the data from both LBL sonar localization and DR to perform the state estimation. Simulation results show that the adaptive UKF has better localization performance than a normal UKF for a deep-sea tracked vehicle (DTV).5) Based on the line of sight navigation method, the desired speed and heading angle of the mine collection vehicle are obtained. Considering the influence of track sliding, a sub-factor that is proportional to the heading error is added, a desired angular velocity model of left and right hydraulic motors set up, the heading angle control of the mine collection vehicle is achieved. Considering the non-liner and uncertain features of a hydraulic system, the analysis model of the angular velocity control system of a hydraulic motor is built and a fuzzy-PID control algorithm is proposed. The simulation results show that:the proposed system can effectively compensate track slip, resist the effect of various disturb factors, and track a desired mining path accurately.