From Data to Safe Mobile Robot Navigation: An Efficient and Modular Robust MPC Design Pipeline

TL;DR

This paper introduces a systematic, data-driven pipeline for designing robust MPC controllers that ensure safe and reliable autonomous mobile robot navigation despite disturbances and noisy measurements.

Contribution

It presents an efficient, modular pipeline that estimates disturbance bounds from data and synthesizes robust output-feedback MPC, with reproducible code and empirical validation.

Findings

Robust constraint satisfaction demonstrated in quadrotor simulations.

Pipeline effectively estimates disturbance bounds from experimental data.

Reproducible code facilitates practical implementation of robust MPC.

Abstract

Model predictive control (MPC) is a powerful strategy for planning and control in autonomous mobile robot navigation. However, ensuring safety in real-world deployments remains challenging due to the presence of disturbances and measurement noise. Existing approaches often rely on idealized assumptions, neglect the impact of noisy measurements, and simply heuristically guess unrealistic bounds. In this work, we present an efficient and modular robust MPC design pipeline that systematically addresses these limitations. The pipeline consists of an iterative procedure that leverages closed-loop experimental data to estimate disturbance bounds and synthesize a robust output-feedback MPC scheme. We provide the pipeline in the form of deterministic and reproducible code to synthesize the robust output-feedback MPC from data. We empirically demonstrate robust constraint satisfaction and recursive feasibility in quadrotor simulations using Gazebo.