Accounting for Parametric Model Uncertainty in Collision Avoidance for Unmanned Vehicles Using Sparse Grid Interpolation

In this paper we present an enhancement of model-based trajectory selection algorithms — a popular class of collision avoidance techniques for autonomous ground vehicles. Rather than dilate a set of individual candidate trajectories in an ad hoc way to account for uncertainty, we generate a set of trajectory clouds — sets of states that represent possible future poses over a product of intervals representing uncertainty in the model parameters, initial conditions and actuator commands. The clouds are generated using the sparse-grid interpolation method which is both error-controlled and computationally efficient. The approach is implemented on a differential drive vehicle.