An Safety-Critical MPC Method with Circulation Inequality for Robot Navigation

This paper proposes a safety-critical model predictive control (MPC) strategy for robot navigation and obstacle avoidance. To ensure collision-free navigation, a novel polyhedral collision avoidance constraint based on a discrete-time control barrier function (DCBF) is incorporated into the MPC optimization framework. Additionally, circulation inequality (CI) constraints are introduced to mitigate potential local stability issues during robot navigation, which also contribute to reducing the optimization time of the framework. Finally, simulation experiments validate the effectiveness of the proposed method.