A Method for Modeling and Prediction of Ground Vehicle Dynamics and Stability in Autonomous Systems

A future limitation of autonomous ground vehicle technology is the inability of current algorithmic techniques to successfully predict the allowable dynamic operating ranges of unmanned ground vehicles. A further difficulty presented by real vehicles is that the payloads may and probably will change with unpredictably time as will the terrain on which it is expected to operate. To address this limitation, a methodology has been developed to generate real-time estimations of a vehicle's instantaneous Maneuvering Manifold. This approach uses force-moment method techniques to create an adaptive, parameterized vehicle model. A technique is developed for estimation of vehicle load state using internal sensors combined with low-magnitude maneuvers. An unscented Kalman filter based estimator is then used to estimate tire forces for use in determining the ground/tire coefficient of friction. Probabilistic techniques are then combined with a combined-slip pneumatic trail based estimator to estimate the coefficient of friction in real-time. This data is then combined to map out the instantaneous maneuvering manifold while applying techniques to account for dynamic rollover and stability limitations. The algorithms are implemented in MATLAB, simulated against TruckSim models, and results are shown to demonstrate the validity of the techniques. The developed methodology is shown to be a novel approach that is capable of addressing the problem of successfully estimating the available maneuvering manifold for autonomous ground vehicles.