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A model predictive control scheme for mobile robotic vehicles in dynamic environments

This paper considers the obstacle avoidance motion planning problem for autonomous vehicles operating in uncertain dynamic environments. By resorting to a set-theoretic approach, a receding horizon control algorithm is developed for robots described by linear time-invariant (LTI) systems subject to disturbance effects, input and state constraints. The resulting scheme guarantees Uniformly Ultimate Boundedness and constraints fulfilment regardless of any obstacle configuration occurrence.