Kinematic modelling of tracked vehicles by experimental identification

The paper proposes a kinematic approach for tracked vehicles in order to improve motion control and pose estimation. Complex dynamics due to slippage and soil shearing make it difficult to predict the exact motion of the vehicle from the velocity of the two tracks. Nevertheless, reliable geometric approximations are necessary to perform onboard real-time computations for autonomous navigation. The presented solution is based on the kinematic similarities between tracked vehicles and wheeled differential drive vehicles. Particularly, the approximate position of wheel contact points for an equivalent vehicle can be optimized for a particular terrain at moderate speeds. This is achieved off-line by feeding a genetic algorithm with raw trajectory data and reliable localization estimations based on external sensors. The method has been successfully tested for online odometric computations and low-level motion control with the Auriga-α mobile robot. Moreover, the identified parameters are similar to those obtained from the simulated stationary response of a complex dynamic model of this vehicle.