Estimation of skid‐steered wheeled vehicle states using STUKF with adaptive noise adjustment

Skid‐steered wheeled vehicles are commonly adopted in outdoor environments with the benefits of mobility and flexible structure. However, different from Ackerman turning vehicles, skid‐steered vehicles do not possess geometric constraint but only dynamic constraint when steered, which leads to motion control and state estimation problems for skid‐steered vehicles. The control-ling accuracy of a skid‐steered vehicle depends largely on feedback state information from sensors and an observer. In this study, a 3‐DOF dynamic model using a Brush nonlinear tire model is built, first, to model a 6 × 6 skid‐steered wheeled vehicle in flat ground driving conditions. Then, an observer using the unscented Kalman filter with a strong tracking algorithm and adaptive noise matrix adjustment (AN‐STUKF) is established to estimate vehicle motion states based on the 3‐DOF dynamic model. Finally, the experiment is carried out in three different driving conditions to verify the accuracy and stability of the proposed method. The results show that the AN‐STUKF method possesses better accuracy and tracking rate than the traditional UKF, and the phenomenon of ICRs shifting forward of the skid‐steered wheeled vehicle is also verified.