Closed-Loop Motion Planning for Differentially Flat Systems: A Time-Varying Optimization Framework

TL;DR

This paper introduces a novel closed-loop motion planning framework for differentially flat systems that integrates control and optimization, enabling real-time, adaptive trajectory generation in dynamic environments.

Contribution

It develops a unified optimization-based control framework that generalizes feedback linearization for differentially flat systems, ensuring convergence to optimal trajectories.

Findings

Proves global asymptotic convergence of the optimization dynamics.

Demonstrates effectiveness through multi-robot tracking example.

Shows improved adaptability in obstacle avoidance scenarios.

Abstract

Motion planning and control are two core components of the robotic systems autonomy stack. The standard approach to combine these methodologies comprises an offline/open-loop stage, planning, that designs a feasible and safe trajectory to follow, and an online/closed-loop stage, tracking, that corrects for unmodeled dynamics and disturbances. Such an approach generally introduces conservativeness into the planning stage, which becomes difficult to overcome as the model complexity increases and real-time decisions need to be made in a changing environment. This work addresses these challenges for the class of differentially flat nonlinear systems by integrating planning and control into a cohesive closed-loop task. Precisely, we develop an optimization-based framework that aims to steer a differentially flat system to a trajectory implicitly defined via a constrained time-varying optimization problem. To that end, we generalize the notion of feedback linearization, which makes non-linear systems behave as linear systems, and develop controllers that effectively transform a differentially flat system into an optimization algorithm that seeks to find the optimal solution of a (possibly time-varying) optimization problem. Under sufficient regularity assumptions, we prove global asymptotic convergence for the optimization dynamics to the minimizer of the time-varying optimization problem. We illustrate the effectiveness of our method with two numerical examples: a multi-robot tracking problem and a robot obstacle avoidance problem.