Research reveals a robust Gaussian Mixture Model application, improving state estimation and motion accuracy in skid-steer vehicles, suggesting better performance under varied terrain interactions.
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 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.
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Salvi et al. (2025) studied this question.
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