Randomized model predictive control for robot navigation

Abstract

The paper suggests a new approach to navigation of mobile robots, based on nonlinear model predictive control and using a navigation function as a control Lyapunov function. In this approach, the nonlinear optimal control problem is treated using randomized algorithms. The advantage of the proposed combination of navigation functions for robot motion planning with randomized algorithms within an MPC framework, is that the control design offers stability by design, is platform independent, and allows the designer to trade-off performance for (computation) speed, according to the application requirements.