A State Estimation Approach for a Skid-Steered Off-Road Mobile Robot

This thesis presents a novel state estimation structure, a hybrid extended Kalman filter/Kalman filter developed for a skid-steered, six-wheeled, ARGO® all-terrain vehicle (ATV). The ARGO ATV is a teleoperated unmanned ground vehicle (UGV) custom fitted with an inertial measurement unit, wheel encoders and a GPS. In order to enable the ARGO for autonomous applications, the proposed hybrid EKF/KF state estimator strategy is combined with the vehicle’s sensor measurements to estimate key parameters for the vehicle. Field experiments in this thesis reveal that the proposed estimation structure is able to estimate the position, velocity, orientation, and longitudinal slip of the ARGO with a reasonable amount of accuracy. In addition, the proposed estimation structure is well-suited for online applications and can incorporate offline virtual GPS data to further improve the accuracy of the position estimates. The proposed estimation structure is also capable of estimating the longitudinal slip for every wheel of the ARGO, and the slip results align well with the motion estimate findings

Adaptive motion planning for a mobile robot

Historically, trapezoidal velocity profiles have been widely used to control engines. Nevertheless, the evolution of robots and their uses has led to the need of using smoother profiles, due to the demand of high precision and delicate movements. It has been shown that this can be achieved by minimizing the change of acceleration and using s-curve profiles. Moreover, to provide a good control of the movement of a robot, it is necessary to ensure that it will meet the desired velocity profile. Therefore, a way to prevent how the wheels will react on the soil becomes highly useful, in order to adapt the supplied torque. This thesis suggests a model to define an appropriate s-curve velocity profile given the desired starting and ending kinematic states for a mobile robot. The study is then focused on a one-wheel system to define the interaction between the soil and a wheel. This interaction is modelled and extended in order to calculate the required torque, drawbar pull and power needed to fulfil the desired s-curve velocity profile. Finally, an introduction to unicycle robots is given as an example of how the proposed models could be applied in the motion planning of a mobile robot. Key words: terramechanics, s-curve, jerk, velocity profileOutgoin

Modeling and Control of the UGV Argo J5 with a Custom-Built Landing Platform

This thesis aims to develop a detailed dynamic model and implement several navigation controllers for path tracking and dynamic self-leveling of the Argo J5 Unmanned Ground Vehicle (UGV) with a custom-built landing platform. The overall model is derived by combining the Argo J5 driveline system with the wheelsterrain interaction (using terramechanics theory and mobile robot kinetics), while the landing platform model follows the Euler-Lagrange formulation. Different controllers are, then, derived, implemented to demonstrate: i.) self-leveling accuracy of the landing platform, ii.) trajectory tracking capabilities of the Argo J5 when moving in uneven terrains. The novelty of the Argo J5 model is the addition of a vertical load on each wheel through derivation of the shear stress depending on the point’s position in 3D space on each wheel. Static leveling of the landing platform within one degree of the horizon is evaluated by implementing Proportional Derivative (PD), Proportional Integral Derivative (PID), Linear Quadratic Regulator (LQR), feedback linearization, and Passivity Based Adaptive Controller (PBAC) techniques. A PD controller is used to evaluate the performance of the Argo J5 on different terrains. Further, for the Argo J5 - landing platform ensemble, PBAC and Neural Network Based Adaptive Controller (NNBAC) are derived and implemented to demonstrate dynamic self-leveling. The emphasis is on different controller implementation for complex real systems such as Argo J5 - Landing platform. Results, obtained via extensive simulation studies in a Matlab/Simulink environment that consider real system parameters and hardware limitations, contribute to understanding navigation performance in a variety of terrains with unknown properties and illustrate the Argo J5 velocity, wheel rolling resistance, wheel turning resistance and shear stress on different terrains

Dynamic modelling of wheel-terrain interaction of a UGV

Understanding the vehicle-terrain interaction is essential for autonomous and safe operations of skid-steering unmanned ground vehicles (UGVs). This paper presents a comprehensive analysis of the dynamic processes involved in this interaction, using the vehicle kinetics and the theory of terramechanics to derive systematically shear displacement, reaction force, and load distribution for a wheel. The new model is then summarized in the form of an algorithm to allow for computation of characteristic performance of the interaction such as slip ratios, rolling resistance, and moment of turning resistance for a number of terrain types. Given the current state of the vehicle and terrain parameters, the model can be used to estimate its next states and to predict the vehicle running path. The development is illustrated by simulation and verified with experimental data. © 2007 IEEE