Collision avoidance for uncertain nonlinear systems with moving obstacles using robust Model Predictive Control

Raffaele Soloperto, Johannes Köhler, Frank Allgöwer, Matthias A. Müller

Abstract: In this paper, we provide a novel robust collision avoidance approach that is based on a general tube-based MPC framework. We consider collision avoidance for general nonlinear uncertain systems with moving obstacles. The resulting optimization problem can be handled by standard nonlinear programming solvers. Moreover, we provide formal guarantees, such as recursive feasibility, constraint satisfaction, as well as robust collision avoidance. We demonstrate the efficacy of the proposed method through a simulation of an autonomous car during realistic manoeuvres.