Derivative-free Kalman filtering for a six-wheeled rover

The aim of this paper is to provide a Derivative-free Kalman Filter (DKF) for a six-wheeled rover, as an alternative to the classical Kalman Filters. The DKF removes the risks which the Extended Kalman Filter (EKF), based on the computation of Jacobians, pose when implemented on platforms with limited numerical precision. It is also more computationally efficient than the Unscented Kalman filters, while retaining similar performance. The DKF is designed for an Ackermann steered six-wheeled rover which has nonlinear, coupled longitudinal and lateral dynamics. Starting from the first principles of the two axle car, we derive the rover’s mathematical model, which we then express through a flat-output representation. Using the resulting Brunovsky form, we provide both a position estimate (via the DKF) and a control module (a reference tracking variation). The model and its ancillary blocks are tested in simulation to validate the flat representation’s consistency and the performance of the navigation and control actions.