A Model Predictive Control Architecture for Autonomous Vehicles Moving in Uncertain Scenarios

This paper addresses the constrained navigation problem for autonomous robots in unknown, cluttered environments, emphasizing safety during online operations. A networked control framework is developed using model predictive control and a set-theoretic approach. The proposed architecture ensures anti-collision capabilities despite communication delays and mission success despite unpredictable obstacles. Formal proofs establish trajectory constraints and uniform boundedness under vehicle uncertainties. The study focuses on skidsteered tracked mobile robots, valued for their flexibility and adaptability in hazardous scenarios. Experiments validate the architecture's effectiveness, demonstrating its advantages in collision avoidance, trajectory regulation, and robust performance in complex, dynamic settings.