Perception-Aware Chance-Constrained Model Predictive Control for Uncertain Environments

We consider a known system that operates in an unknown environment, which is discovered by sensing and affects the known system through constraints. However, sensing quality is typically dependent on system operation. Thus, the control decisions should account for both the impact of control on sensing and the impact of sensing on control. Since the information acquired from sensing is of statistical nature, we develop a perception-aware chance-constrained model predictive control (PAC-MPC) strategy that leverages uncertainty propagation models to relate control and sensing decisions to the environment knowledge. We propose conditions for recursive feasibility and provide an overview of the stability properties in such a statistical framework. The performance of the proposed PAC-MPC is demonstrated on a case study inspired by an automated driving application.