Suksun Hutangkabodee

Performance prediction of a wheeled vehicle on unknown terrain using identified soil parameters

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 Simpsons 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

Soil Parameter Identification and Driving Force Prediction for Wheel-Terrain Interaction:

This paper considers wheeled vehicles traversing unknown terrain, and proposes an approach for identifying the unknown soil parameters required for vehicle driving force prediction (drawbar pull prediction). The predicted drawbar pull can potentially be employed for traversability prediction, traction control, and trajectory following which, in turn, improve overall performance of off-road wheeled vehicles. The proposed algorithm uses an approximated form of the wheel-terrain interaction model and the Generalized Newton Raphson method to identify terrain parameters in real-time. With few measurements of wheel slip, i, vehicle sinkage, z, and drawbar pull, DP, samples, the algorithm is capable of identifying all the soil parameters required to predict vehicle driving forces over an entire range of wheel slip. The algorithm is validated with experimental data from a wheel-terrain interaction test rig. The identified soil parameters are used to predict the drawbar pull with good accuracy. The technique presented in this paper can be applied to any vehicle with rigid wheels or deformable wheels with relatively high inflation pressure, to predict driving forces in unknown environments.

The modelling and estimation of driving forces for unmanned ground vehicles in outdoor terrain

Ground vehicles traversing rough unknown terrain has many applications in a range of industries including agriculture, defence, mining, space exploration and construction. The interaction dynamics between the vehicle and the terrain play a crucial role in determining the mobility characteristics of the vehicle. The two critical parameters that influence the interaction dynamics are the wheel/track slip and the unknown soil parameters. An algorithm for identifying unknown soil parameters based on a dynamic model and sensor feedback is presented. A method for estimating vehicle slip parameters based on an optical flow algorithm and a sliding mode observer is also presented. The last section addresses the traversability prediction for tracked vehicles traversing in circular trajectories. The algorithms are developed for both tracked and wheeled vehicles. The algorithms are tested and evaluated using two specially designed test rigs, and the test results are presented in the paper.

Multi-solution Problem for Track-Terrain Interaction Dynamics and Lumped Soil Parameter Identification

A technique for identifying lumped soil parameters on-line while traversing with a tracked unmanned ground vehicle (UGV) on an unknown terrain is presented. This paper shows the multi-solution problem when identification of soil parameters — cohesion (c), shear deformation modulus (φ), and shear deformation modulus (K) are to be attempted using the track-terrain interaction dynamics model. The initiation of the idea of lumping the cohesion and internal friction angle terms and treating them as a single parameter to solve this problem is presented. The technique used for lumped soil parameter identification is based on the Newton Raphson method. This method is proved to be very effective in terms of prediction accuracy, computational speed, and robustness to initial conditions and noise. These identified lumped soil parameters can be used to increase the autonomy of a tracked UGV. The technique presented in this paper is general and can be applied to any tracked UGV.

Validation of soil parameter identification for track-terrain interaction Dynamics

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.

Model-based soil parameter identification for wheel-terrain interaction dynamics

Abstract This paper presents an algorithm for identifying soil parameters for wheel-terrain interaction dynamics. The soil parameters are useful for traversability prediction, traction control, and performance optimization of a wheeled vehicle traveling on unknown terrain. The Composite Simpsons Rule (CSR) is employed to approximate integrals of the full wheel-terrain interaction dynamic model. This is to facilitate the implementation of soil parameter identification on this model and allow fast identification speed. The 2-stage iterative Newton Raphson (NR) method is used for soil parameter identification. Simulation results show successful identification of a complete set of soil parameters with relatively fast speed. The approach in this paper has great potential to be applied for real off-road wheeled vehicle.

Traversability prediction for unmanned ground vehicles based on identified soil parameters

Abstract A novel technique for identifying soil parameters on-line while traversing with a tracked vehicle on unknown terrain is presented. This technique, based on the Newton Raphson method is used to identify unknown soil parameters. Comparing with the Least Square method, it shows that the Newton Raphson method is better in terms of prediction accuracy, computational speed, and robustness to initial conditions and noise. For heavy tracked vehicle, cohesion has negligible effect on the vehicle performance. These identified soil parameters are then employed for traversability prediction for a tracked vehicle travelling on unknown terrain.