Model predictive control with safety barrier enforcement for dynamic obstacle avoidance of mobile robots

Abstract

This research proposes a model predictive control (MPC) approach with additional barrier function constraint for safe navigation of a differential drive wheeled mobile robot (DDWMR) in the presence of static and dynamic obstacles. The proposed approach uses the kinematic model of DDWMR to initially constructs a stabilizing MPC on the basis of Lyapunov’s stability theory. To ensure safe navigation of the DDWMR when obstacles are present, the control barrier function (CBF) concept is subsequently constructed and integrated into the developed MPC framework. The integrated MPC-CBF approach is shown to guarantee both the stability and safety properties of the DDWMR while navigating towards a desired goal position through a workspace filled with obstacles. The good performance of the proposed framework is demonstrated through computer simulations and experimental validation on a Turtlebot3 DDWMR plat form. In the real robot experiments, the controller achieved final tracking errors of [e_x e_y] = [0.1286 0.0626] m and e₀ = 0.021 rad, while the corresponding simulation errors were [e_x e_y] = [0.0824 0.0698] m and e₀ = 0.0883 rad, respectively. These results demonstrate that the developed feedback control method ensures safe, stable, and collision-free motions of the DDWMR.