A Novel State Estimation Algorithm for Wheel-legged Multi-mode Vehicle

Wheel-legged multi-mode vehicles exhibit the capability to effectively execute specialized tasks in complex environments, relying on the maintenance of high maneuverability control. State estimation serves as the foundation for achieving this dynamic control. This paper proposes a novel state estimation algorithm that considers wheel dynamic and reliable contact state based Kalman Filter (CWRKF) for multi-mode vehicle, mainly focusing on wheel velocity estimation and vehicle state estimation. The CWRKF integrates wheel velocity estimation to address the issue of inaccurate velocity estimation during driving and braking stages resulting from wheel slip. Simultaneously, a trust model is implemented to ensure that motion calculations are performed based on the reliable contact between the wheels and the ground. Various numerical and experimental simulations are carried out on simulation and actual vehicle platforms to verify the effectiveness of the proposed algorithm. The results demonstrate that the CWRKF algorithm has superior accuracy and robustness in various scenarios, making it a promising approach for state estimation in multi-mode vehicles.