Randomized model predictive control for robot navigation

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.

Authors

• Jorge L. Piovesan
• Herbert G. Tanner

Keywords

• Predictive models
• Predictive control
• Navigation
• Algorithm design and analysis
• Mobile robots
• Lyapunov method
• Optimal control
• Robot motion
• Motion planning
• Control design
• Model Predictive Control
• Robot Navigation
• Optimization Problem
• Control Problem
• Nonlinear Programming
• Path Planning
• Mobile Robot
• Optimal Control Problem
• Nonlinear Model Predictive Control
• Navigation Function
• Model Predictive Control Framework
• Control Samples
• Nonlinear Systems
• Control Input
• Feedback Control
• Workspace
• Closed-loop System
• Phase Control
• Flight Phase
• State Constraints
• Prediction Horizon
• Prediction Phase
• Control Horizon
• Input Constraints
• Finite Interval
• Terminal Cost
• Model Predictive Control Problem
• Model Predictive Control Algorithm