Zibin Song

Non-linear observer for slip estimation of skid-steering vehicles

Accurate estimation of slip is essential in developing autonomous navigation strategies for mobile vehicles operating in unstructured terrain. In this paper, a sliding mode observer is firstly constructed to estimate slip parameters based on the kinematics model of a skid-steering vehicle and trajectory measurement. The stability of the sliding mode observer is given in a mathematical context. Slip estimation schemes using an extended Kalman filter and direct mathematical inversion of the kinematic equations are also presented for comparison purposes. It is shown that the non-linear sliding mode observer is more accurate than the other two methods. The robustness and superior performance of the sliding mode observer is demonstrated using both simulation and experimental results. A camera based system is used to measure the vehicle trajectory during experimental validation

Optical flow-based slip and velocity estimation technique for unmanned skid-steered vehicles

This paper proposes a novel technique to estimate slips and velocities of an unmanned skid-steered vehicle. An optical flow-based visual sensor looking down the terrain surface is employed to recover the motion of the vehicle by tracking features selected from the terrain surface. The special orientation of the on-board camera is to assure high accuracy of the motion estimation. To cope with the noise and uncertainty from the visual sensor, a sliding mode observer (SMO) based on the kinematic model of the skid-steered vehicle is delicately designed to simultaneously estimate the slips and velocities. The complete non-GPS slip and velocity estimation technique is independent of terrain parameters and robust to noise and uncertainty. The SMO scheme can produce more accurate estimates than the extended Kalman filter (EKF) in the nonlinear case. Experimental results are given to show that the technique has good potential for vehicle slip and velocity estimation.

A robust slip estimation method for skid-steered mobile robots

This paper presents a robust slip estimation method for skid-steered mobile robots when they traverse over rough terrain. An optical flow-based visual sensor looking down the terrain surface is employed to recover motion of a mobile robot by tracking features selected from the terrain surface. The motion states of the mobile robot are initially estimated by the visual sensor, however, the estimates are prone to noise and uncertainty which degrades the accuracy and robustness of estimation. To cope with the noise and uncertainty from the visual sensor, a sliding mode observer (SMO) based on the kinematics model of the skid-steered mobile robot is delicately designed to simultaneously estimate slip parameters. The SMO scheme can give more accurate estimates than the extended Kalman filter (EKF) when the slip of the mobile robot has significant changes at abrupt steering. The complete slip estimation method is independent of terrain parameters and robust in the presence of noise and uncertainty. Experimental results show that the method has confident potential for slip estimation of skid-steered mobile robots.

Visual Odometry for Velocity Estimation of UGVs

An accurate and robust velocity estimation method based on an optical flow technique is presented in this paper. Using image sequences captured by a monocular camera mounted under an UGV (unmanned ground vehicle), image velocities are obtained from the optical flow technique. Combining with a camera model, the velocities of the UGV are directly estimated. This velocity estimation method is validated over various types of terrain surfaces, such as coarse sand, fine sand and mixture of coarse sand and gravel. Experimental results show that estimated velocities have very good agreement with measured velocities. Height between the projection center of camera and the terrain surface is proved to be a key parameter in velocity estimation. Height compensation is implemented to give accurate velocity estimation results. Velocity estimation method proposed has many potential applications including localization and slip estimation for UGVs.

The modelling and estimation of driving forces for unmanned ground vehicles in outdoor terrain

Ground vehicles traversing rough unknown terrain has many applications in a range of industries including agriculture, defence, mining, space exploration and construction. The interaction dynamics between the vehicle and the terrain play a crucial role in determining the mobility characteristics of the vehicle. The two critical parameters that influence the interaction dynamics are the wheel/track slip and the unknown soil parameters. An algorithm for identifying unknown soil parameters based on a dynamic model and sensor feedback is presented. A method for estimating vehicle slip parameters based on an optical flow algorithm and a sliding mode observer is also presented. The last section addresses the traversability prediction for tracked vehicles traversing in circular trajectories. The algorithms are developed for both tracked and wheeled vehicles. The algorithms are tested and evaluated using two specially designed test rigs, and the test results are presented in the paper.

VISION-BASED VELOCITY ESTIMATION FOR UNMANNED GROUND VEHICLES

A novel vision-based velocity estimation technique is presented in this paper. A monocular camera rigidly attached to an unmanned ground vehicle (UGV) is used to capture image sequences of the terrain surface and compute the image velocities using an optical flow method. Combining with the proposed camera model, the velocity of the UGV can be directly estimated. This velocity estimation method is validated over coarse sand, fine sand and mixture of coarse sand and gravel separately. Estimated velocities are compared to measured velocities from highly accurate optical encoders, showing the maximum error is less than 1.5%. The effect of feature window size and the distance between the camera projection center and the terrain surface on the velocity estimation is investigated. Random white noise is added to test the robustness of the algorithm and the results are encouraging. The proposed velocity estimation method has many promising potential applications.

Slip parameter estimation of a single wheel using a non-linear observer

Sliding mode observer is a variable structure system where the dynamics of a nonlinear system is altered via application of a high-frequency switching control. This paper presents a non-linear sliding mode observer for wheel linear slip and slip angle estimation of a single wheel based on its kinematic model and velocity measurements with added noise to simulate actual on-board sensor measurements. Lyapunov stability theory is used to establish the stability conditions for the observer. It is shown that the observer will converge in a finite time, provided the observer gains satisfy constraints based on a stability analysis. To validate the observer, linear and two-dimensional (2D) test rigs are specially designed. The sliding mode observer is tested under a variety of conditions and it is shown that the sliding mode observer can estimate wheel slip and slip angle to a high accuracy. It is also shown that the sliding mode observer can accurately predict wheel slip and slip angle in the presence of noise, by testing the performance of the sliding mode observer after adding white noise to the measurements. An extended Kalman filter is also developed for comparison purposes. The sliding mode observer is better in terms of prediction accuracy.

Non-linear observer for slip estimation of tracked vehicles

Abstract Accurate estimation of slip is essential in developing autonomous navigation strategies for mobile vehicles operating in unstructured terrain. This paper presents an accurate and robust technique for the estimation of slip parameters of a tracked vehicle. The technique uses a sliding mode observer (SMO) with sprocket wheel angular velocities and vehicle trajectory as inputs and estimates slip parameters by minimizing the errors between the predicted trajectory and the measured trajectory. The error dynamics is proven to converge after a finite time from any arbitrary point in error space and remains in the neighbourhood of the sliding motion. Slip estimation schemes using an extended Kalman filter (EKF) and direct mathematical inversion of the kinematic equations are also presented for comparison purposes. It is shown that the non-linear SMO is more accurate than the other two methods. The robustness and superior performance of the SMO are demonstrated using both simulation and experimental results. A specially designed test rig is used for accurate control and measurement of track slips during the experimental validation of the proposed observer.

Driver support system based on a non-linear slip observer for off road vehicles

Abstract Off road ground vehicles have many potential applications, including space, defence, agriculture, mining and construction. Increased autonomy of ground vehicles will not only improve the safety of the operators but also assist in trajectory tracking. Accurate estimation of slip is essential in developing autonomous navigation strategies for mobile off road vehicles operating in unstructured terrain. In this paper, a driver assistance and vehicle control capability to increase safety and efficiency by means of non-linear slip estimation is presented. A sliding mode observer and an Extended Kalman Filter are constructed to estimate slip parameters based on the kinematics model of a tracked vehicle and trajectory measurement. Autonomous driver support system for the rural road environment (off road) using estimated slip parameters is investigated.

Non-linear Observer for Slip Parameter Estimation of Unmanned Wheeled Vehicles

This paper presents a non-linear sliding mode observer (SMO) for the estimation of wheeled vehicle slip parameters based on the vehicle kinematic model and on-board sensor inputs. Lyapunov stability theory is used to establish the stability conditions for the observer. It is shown that the observer will converge in a finite time, provided the observer gains satisfy constraints based on the stability analysis. Since vehicle position and velocity can not be very accurately measured using on-board sensors, specially designed linear and 2D test rigs are used to validate the proposed observer. The SMO is tested under a variety of conditions and it is shown that the SMO can estimate the slip parameters to a high accuracy. It is also shown that the SMO can accurately predict the slip parameters in the presence of noise by testing the SMO after adding white noise to the measurements. An extended Kalman filter is presented for the purpose of comparison. Thus the proposed observer has the potential to be used on unmanned ground vehicles equipped with sensing systems such as GPS or inertial sensors.