Integrating Planning and Predictive Control Using the Path Feasibility Governor

TL;DR

This paper introduces the Path Feasibility Governor (PathFG), a modular framework that seamlessly integrates path planning with nonlinear MPC to ensure safe, stable, and efficient autonomous navigation in complex environments.

Contribution

The paper presents the PathFG, a novel approach that enhances integration of planning and control, improving computational efficiency and safety guarantees in autonomous systems.

Findings

Proves safety and asymptotic stability of PathFG.

Reduces prediction horizon requirements for MPC.

Demonstrates real-time quadrotor navigation in cluttered environments.

Abstract

The motion planning problem of generating dynamically feasible, collision-free trajectories in non-convex environments is a fundamental challenge for autonomous systems. Decomposing the problem into path planning and path tracking improves tractability, but integrating these components in a theoretically sound and computationally efficient manner is challenging. We propose the Path Feasibility Governor (PathFG), a framework for integrating path planners with nonlinear Model Predictive Control (MPC). The PathFG manipulates the reference passed to the MPC controller, guiding it along a path while ensuring constraint satisfaction, stability, and recursive feasibility. The PathFG is modular, compatible with replanning, and improves computational efficiency and reliability by reducing the need for long prediction horizons. We prove safety and asymptotic stability with a significantly expanded region of attraction, and validate its real-time performance through a simulated case study of quadrotor navigation in a cluttered environment.