Characterizing Gaussian Mixture of Motion Modes for Skid-Steer Vehicle State Estimation

Abstract The skidding and slipping motion of skid-steered wheel mobile robots (SSWMRs) is highly influenced by the complex nature of tire–terrain interactions. Lack of reliable terrain friction models cascade into unreliable motion models, especially the reduced-ordered variants used for state estimation and robot control. Ensemble modeling is an emerging research direction where the overall motion model is broken down into a family of local models to distribute the performance and resource requirement and provide a fast real-time prediction. To this end, a Gaussian mixture model (GMM)-based modeling approach for the identification of model clusters is adopted and implemented within an interactive multiple model (IMM)-based state estimation framework. The methodology is applied and investigated for estimating angular velocity for a mid-scale skid-steered wheel mobile robot platform.