Ukf

With robust structure and high maneuverability, skid-steered vehicles have been widely used in terrain exploration, construction, rescue and relief fields. Inevitable slipping and sliding of the tire that makes the vehicle status difficult to obtain. In this paper, an equivalent differential driven kinematic model is proposed. Additionally, the mapping relationship of slippage coefficient defined in the original 4-wheel unmanned skid-steered vehicle and the wheel location in the equivalent model are discussed. The proposed equivalent wheel location obtains explicit physical significance. The slippage of the tire has also been calculated by estimating the position of instantaneous center of rotation (ICR) of the wheels from multi-sensors. An unscented Kalman filter (UKF) based fusion method is adapted to obtain the vehicle status. A prototype vehicle named DUBHE is adapted to verify the reliability of the informed method. Preliminary experimental results are compared to demonstrate the effectiveness of the method in different scenarios.

In this paper we introduce a cubic root unscented kalman filter (CRUKF) compared to the unscented kalman filter (UKF) for calculating the covariance cubic matrix and covariance matrix within a sensor fusion algorithm to estimate the measurements of an omnidirectional mobile robot trajectory. We study the fusion of the data obtained by the position and orientation with a good precision to localize the robot in an external medium; we apply the techniques of kalman filter (KF) to the estimation of the trajectory. We suppose a movement of mobile robot on a plan in two dimensions. The sensor approach is based on the CRUKF and too on the standard UKF which are modified to handle measurements from the position and orientation. A real-time implementation is done on a three-wheeled omnidirectional mobile robot, using a dynamic model with trajectories. The algorithm is analyzed and validated with simulations. 1. INTRODUCTION The mobile robots are equipped with sensors to measure the distance of the robot from the space of the objects where they move. Measurements which are always linked by noises are then fused together in a filter to obtain an estimate of the position and orientation of the mobile robot trajectory in the space and plan [1, 2]. The most important problems related to robots are situated in sensors powere by their battery which reduces the autonomy. Also there are situations where sensors cannot function simultaneously, when they use the same frequency band [3, 4]. The Kalman filter is an optimal linear estimator when the process noise and the measurement noise can be modeled by white Gaussian noise. Non-linear problems can be solved with the unscented Kalman filter (UKF) and we propose a new algorithm that is called the cubic root unscented Kalman filter (CRUKF) to solve also nonlinear problems. In our work, we use this technique to estimate the position and the angle of the robot in its displacement then a comparison between the performances of the two filters is presented to give an evaluation of more precised measurement and will appreciate better statistical properties [2, 5]. This paper is organized as follow; we present the modeling of the mobile robot with the equations of trajectory and angles of the robot. Next, we pass to the presentation of the UKF algorithm and we propose a CRUKF algorithm to improve the accuracy of the state estimate. Finally we present and discuss the simulation results and outline some possible extensions for future investigations.