A Receding Horizon Control Strategy for Autonomous Vehicles in Dynamic Environments

Abstract

In this brief, a receding horizon control scheme for solving the obstacle avoidance motion planning problem of autonomous vehicles operating in uncertain dynamic environments is developed. By considering robots described by discrete-time linear time-invariant models, we propose a set-theoretic-based architecture to deal with disturbance effects, and input and state constraints. As one of its main merits, the strategy is capable of taking care of time-varying obstacle scenarios by jointly exploiting robust positively invariant region and one-step controllable set concepts. The resulting scheme guarantees uniformly ultimate boundedness and constraints fulfilment regardless of any admissible obstacle configuration.