Vehicle-Terrain Parameter Estimation for Small-Scale Unmanned Tracked Vehicles

Unmanned vehicles are used extensively in a wide range of industries: from agriculture, mining, search and rescue, to hazardous waste management and disposal [1]. One common thread to these applications is that in these instances, the vehicle must traverse widely uncertain, highly unstructured terrain. Thus, having prior information or the ability to estimate vehicle-terrain interaction parameters as the vehicle moves along uncertain terrain provides the opportunity to optimize traction and/or power consumption in order to meet the increasingly complex, highly-critical mission objectives of today's robotic applications. This is particularly significant for tracked vehicles, which rely on skid-steering, and thus the deformability of the terrain, to achieve locomotion. Terrain parameters, such as internal friction angle and soil cohesion play a critical role in estimating track-terrain interaction from readily available odometry from wheel encoders and navigation sensors, such as gyroscopes and accelerometers. The work presented here establishes a need for a reliable means for estimating terrain parameters, particularly for deformable terrain. Methods, such as Extended Kalman Filters and Newton Raphson techniques for estimating vehicle-terrain parameters have been implemented and evaluated using experimental studies. The estimated results obtained are compared with experimental data and tested on uniform terrain using a small-scale unmanned tracked robot.