Online terrain estimation for autonomous vehicles on deformable terrains. (October 2020)

• Record Type: Journal Article
• Authors: Dallas, James; Jain, Kshitij; Dong, Zheng; Sapronov, Leonid; Cole, Michael P.; Jayakumar, Paramsothy; Ersal, Tulga
• Is Part Of: Journal of terramechanics. Volume 91(2020)
• Page Start: 11
• Page End: 22
• Publication Date: 2020-10
• Subjects: Terramechanics -- Parameter estimation -- Wheeled vehicles -- Deformable terrain -- Control -- Kalman filter; Trafficability -- Periodicals; Praticabilité (Routes) -- Périodiques; Trafficability; Periodicals; 629.222
• DOI: 10.1016/j.jterra.2020.03.001
• Languages: English
• ISSNs: 0022-4898

Abstract

Highlights: A new surrogate model is developed to efficiently capture the Soil Contact Model. The surrogate model is combined with Kalman filtering for online estimation. Kalman filtering recovers terrain parameters online with standard vehicle sensors. Terrain estimation can reduce bicycle model prediction errors. Abstract: In this work, a terrain estimation framework is developed for autonomous vehicles operating on deformable terrains. Previous work in this area usually relies on steady state tire operation, linearized classical terramechanics models, or on computationally expensive algorithms that are not suitable for real-time estimation. To address these shortcomings, this work develops a reduced-order nonlinear terramechanics model as a surrogate of the Soil Contact Model (SCM) through extending a state-of-the-art Bekker model to account for additional dynamic effects. It is shown that this reduced-order surrogate model is able to accurately replicate the forces predicted by the SCM while reducing the computation cost by an order of magnitude. This surrogate model is then utilized in an unscented Kalman filter to estimate the sinkage exponent. Simulations suggest this parameter can be estimated within 4% of its true value for clay and sandy loam terrains. It is also shown in simulation and experiment that utilizing this estimated parameter can reduce the prediction errors of the future vehicle states by orders of magnitude, which could assist with achieving more robust model-predictive autonomous navigation strategies.