Unmanned ground vehicle-unmanned aerial vehicle relative navigation robust adaptive localization algorithm

The unmanned aerial vehicle (UAV) and the unmanned ground vehicle (UGV) can complete complex tasks through information sharing and ensure the mission execution capability of multiple unmanned carrier platforms. At the same time, cooperative navigation can use the information interaction between multi-platform sensors to improve the relative navigation and positioning accuracy of the entire system. Aiming at the problem of deviation of the system model due to gross errors in sensor measurement data or strong manoeuvrability in complex environments, a robust and adaptive UGV-UAV relative navigation and positioning algorithm is proposed. In this paper, the relative navigation and positioning of UGV-UAV is studied based on inertial measurement unit (IMU)/Vision. Based on analyzing the relative kinematics model and sensor measurement model, a leader (UGV)-follow (UAV) relative navigation model is established. In the implementation of the relative navigation and positioning algorithm, the robust adaptive algorithm and the non-linear Kalman filter (extended Kalman filter [EKF]) algorithm are combined to dynamically adjust the system state parameters. Finally, a mathematical simulation of the accompanying and landing process in the UGV-UAV cooperative scene is carried out. The relative position, velocity and attitude errors calculated by EKF, Robust-EKF and Robust-Adaptive-EKF algorithms are compared and analyzed by simulating two cases where the noise obeys the Gaussian distribution and the non-Gaussian distribution. The results show that under the non-Gaussian distribution conditions, the accuracy of the Robust-Adaptive-EKF algorithm is improved by about two to three times compared with the EKF and Robust-EKF and can almost reach the relative navigation accuracy under the Gaussian distribution conditions. The robust self-adaptive relative navigation and positioning algorithm proposed in this paper has strong adaptability to the uncertainty and deviation of system modelling in complex environments, with higher accuracy and stronger robustness.