Soil Identification and Control-Parameter Estimation for Optimal Driving of Wheeled Robots on Rough Terrain

Abstract

Wheeled robots on rough terrain are needed to effectively change wheel control strategies since optimal slip and maximum traction levels differ depending on soil types such as sandy soil, grassy soil or firm soil. In a view point of wheel control, this paper focuses on a prediction method of optimal control parameters such as optimal slip ratio and traction coefficient acting on wheels to maximize traction or minimize energy consumption. In this paper, optimal control parameter (OCP) models based on surface reaction index (SRI) are experimentally derived using characteristic data from wheel-soil interaction through indoor experiments by a testbed for analysis of wheel-soil interaction on three types of soil; grass, gravel and sand. For estimating surface reaction index (SRI), actual traction coefficient, including information of motion resistance, is observed by a state estimator which is constructed from longitudinal wheeled robot dynamics. The actual traction coefficient and slip ratio on wheels are employed to estimate surface reaction index (SRI) by a numerical method on the basis of derived optimal models. The proposed algorithm is verified through outdoor driving experiments of a wheeled robot on various types of soil.