In situ identification of shearing parameters for loose lunar soil using least squares support vector machine

Abstract

A method is presented for the online prediction of the terrain-shearing parameters for a wheeled Unmanned Ground Vehicles (UGVs) traversing on an unknown terrain. The method uses a trained multiple-output least squares support vector machine (LS_SVM) to map engineering data and predict the terrain-shearing parameters such as cohesion, internal friction angle and shear deformation modulus without requiring information on wheel sinkage. The predicted terrain-shearing parameters can be used to predict vehicle drawbar pull which can be used for trafficability prediction, traction control and performance optimization. Experiments were performed using a single-wheel soil bin to measure the sinkage, drawbar pull and torque for a griddle net wheel under different slip ratio. An additional experiment was performed under a continuous slip ratio from 0.2 to 0.6 with a wheel load of 50 N to validate the method. The experimental results show that the multiple output LS_SVM model can accurately predict the terrain-shearing parameters using the slip ratio, torque and wheel load without the need of wheel sinkage.