Work

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

Year: 2025

Type: article

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.

Source: ASME Letters in Dynamic Systems and Control

DOI: 10.1115/1.4069445

Authors: Ameya Salvi, Mark Brudnak, Jonathon Smereka, Matthias Schmid, Venkat Krovi

Institutions: Clemson University, United States Army Combat Capabilities Development Command, United States Army

Language: English

Topic: Control and Dynamics of Mobile Robots

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: Automotive Research Center

Award: G3031071254