Real-time estimation of maximum friction and optimal slip ratio based on material identification for a mobile robot on rough terrain

This paper focuses on real-time estimation of optimal control parameters (maximum friction coefficient and optimal slip ratio) in order to secure maneuverability of a mobile robot on rough terrain. This paper is largely divided into two parts; 1) material identification, 2) estimation of optimal control parameters. Firstly, since maximum friction coefficient and optimal slip ratio indicate different characteristics depending on material types, prior to estimation of optimal control parameters, it is needed to identify which material a robot is moving on. Thus, this paper proposes a method for material identification based on soil resistance impeding motion of a robot. Material identification includes Gaussian classifier to stochastically identify one of the material types. Secondly, an estimator is developed so as to predict maximum friction coefficient and optimal slip ratio which are crucial parameters for a mobile robot while effectively traversing rough terrain. Friction-slip curves based on experimental data from a test for analysis of a wheel-terrain interaction are employed to make a prediction model for estimation of optimal control parameters. Results of material identification and estimation of optimal control parameters are verified through one-wheel driving experiments on three kinds of material types: sand, gravel and grass using the wheel-terrain interaction testbed.