This paper considers a tracked vehicle traversing unknown terrain, and proposes an approach based on the Generalized Newton Raphson (GNR) method for identifying all the unknown soil parameters required for tractive force prediction. For the first time, the methodology, based on measurements of track slip, i, and tractive force, F, to find unknown soil parameters is developed. The tractive force is the force generated by a tracked vehicle to drive itself forwards. This tractive force depends to a large extent on certain soil parameters, namely soil cohesion (c), soil internal friction angle (phi), and soil shear deformation modulus (K). Accurately identifying parameters of the soil on which a tracked vehicle is moving will potentially lead to accurate traversability prediction, effective traction control, and precise trajectory tracking. The soil parameter identification algorithm is validated with the experimental data from Wong [3] and from in-house track- terrain interaction test rig showing good identification accuracy and fast execution speed. It is also shown to be relatively robust to initial condition. The identified soil parameters are, in turn, used to predict the tractive forces showing good agreement with all the experimental data. The technique presented in this paper is general and can be applied to any tracked vehicle.

This paper presents a novel technique for identifying soil parameters for a wheeled vehicle travelling on an unknown terrain. The identified soil parameters are required for predicting vehicle drawbar pull and wheel drive torque which can be employed for traversability prediction, traction control, and performance optimization of a wheeled vehicle on unknown terrain. The Newton Raphson method is used as the identification technique applied on the modified form of the wheel-soil interaction dynamics model using the composite Simpson's rule. This work focuses on identifying the internal friction angle, the shear deformation modulus, and the lumped pressure-sinkage coefficient. The fourth parameter, cohesion, does not influence the vehicle drawbar pull and is assigned an average value during the identification process. In an experimental study, the identified parameters are compared with known values, and shown to be in good agreement. Soil parameter identification can be carried out on-line and thus our approach is suitable for real-time applications. The robustness of the method is also shown to be relatively good. The identified soil parameters can be used to predict drawbar pull and wheel drive torque with good accuracy