Set-Based Predictive Control for Collision Detection and Evasion

We propose a set-based predictive control frame-work to predict inbound dynamic obstacles and optimize trajectories in the interest of safely guiding a vehicle towards a target. To account for uncertainties, the set-based controller generalizes conventional model predictive control and predicts the set that the state of a dynamical system might belong to. This generalization is used to formulate collision avoidance as a hard constraint in the set-based predictive control algorithm. As a proof-of-concept, the proposed framework is applied to a ground vehicle attempting to reach a target while anticipating and evading collisions with obstacles in the operating environment. Other applications of the controller and the associated optimal control problem are discussed.