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 do not possess geometric constraint but only dynamic when steered, which leads to motion control state estimation problems for vehicles. The controlling accuracy a vehicle depends largely on feedback information sensors an observer. In this study, 3-DOF model using Brush nonlinear tire is built, first, 6 × flat ground driving conditions. Then, observer unscented Kalman filter strong tracking algorithm adaptive noise matrix adjustment (AN-STUKF) established estimate states based model. Finally, experiment carried out three conditions verify stability proposed method. results show that AN-STUKF method possesses better rate than traditional UKF, phenomenon ICRs shifting forward also verified.