A Robust Model-Predictive Guidance System for Autonomous Vehicles in Cluttered Environments

Abstract

This paper proposes a system for guidance of autonomous vehicles based on a generalized robust model-predictive control (R-MPC) technique. It offers two scenarios, in which R-MPC is used to provide optimized motion plans guaranteed to satisfy constraints and avoid obstacles in the presence of bounded uncertainty. The first scenario involves a military unmanned aerial vehicle flying over a target, while avoiding enemy defenses. The second scenario involves an assistive care robot safely navigating to waypoints throughout a cluttered home. In each case, obstacles are represented as a collection of linear inequality constraints that adapt to changes in the environment. Results show that when R-MPC is formulated using a series of positive-semidefinite relaxations on linear inequality constraints, safe optimum trajectories can be planned by solving a convex quadratic program. When formulated in terms of perturbations on feedback predictions, this solution is guaranteed to be robustly stable.