Modeling and Motion Planning for Autonomous Skid-Steered Vehicles

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This research focuses on developing and experimentally verifying dynamic models for 3D motion of skid-steered wheeled vehicles, a large and important class of outdoor vehicles, which are often preferred for all-terrain mobility due to a robust mechanical structure. In addition, the research will employ the developed 3D models in motion planning. The modeling is based on rigorous dynamic analysis, but requires experimentation to determine key parameters, particularly the shear deformation modulus, the coefficients of friction, and the rolling resistance. The research also emphasizes the inclusion of the motor speed controllers to obtain closed-loop models that are less sensitive to model uncertainty and can hence provide better predictions of vehicle performance for motion planners. The primary motion planning methodology used in this research is called Sampling Based Model Predictive Control and is a variant of model predictive control that takes advantages of the computational speed of sampling methods.Skid-steered autonomous ground vehicles (AGVs) are expected to be used extensively in unstructured, outdoor, undulating environments for tasks such as search and rescue, homeland security, fire fighting, reconnaissance, and surveillance. These tasks will greatly benefit from efficient motion planning for the AGVs, which can be accomplished by using SBMPC in conjunction with the dynamic models of the vehicles developed in this research. The end result is time-optimal trajectories, energy-efficient motion planning, reliable energy predictions for mission completion and refueling, motion planning in the presence of non-fatal mechanical failures (e.g., flat tires), and less frequent replanning due to more accurate predictions of vehicle motion. The detailed modeling, control and planning procedures are expected to be applicable to the growing and increasingly important class of skid-steered, unique mobility platforms such as RHex and Wheg robots, which are expected to find increasing use due to more flexible mobility, e.g., the ability to climb complex obstacles.