A method is presented for using an extended Kalman filter with state noise compensation to estimate the trajectory, orientation, and coefficients of friction for a small-scale robotic tracked vehicle. The ultimate goal of the method is to enable terrain property estimation particularly during laboratory and field-testing utilizing onboard sensor data and/or other sources. A methodology is described that relies on kinematic and dynamic models for skid-steering, as well as tractive force models parameterized by key soil parameters. Favorable results for estimating coefficients of friction are presented based on experimental data collected for laboratory and field testing with an iRobot® PackBot™. Preliminary results confirm the dependence of coefficients of friction on vehicle trajectory turning radius and velocity.

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