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

2025-08-09

AI Summary

To address the degradation of safety in autonomous mobile robot navigation under model disturbances and measurement noise, this paper proposes a data-driven iterative robust model predictive control (MPC) design framework. Departing from heuristic assumptions on disturbance bounds, the method online estimates tight, compact disturbance sets from closed-loop experimental data and systematically incorporates them into output-feedback MPC synthesis, thereby ensuring state constraint satisfaction and recursive feasibility. Key contributions include: (1) a modular, reproducible robust MPC design workflow; (2) closed-loop co-optimization of disturbance estimation and controller synthesis; and (3) empirical validation in a Gazebo quadrotor simulation under realistic noise, demonstrating enhanced safety, robustness, significantly improved constraint satisfaction rates, and superior operational stability.

Technology Category

• Intelligent Robots: State Estimation
• Natural Language Processing: Safety and Robustness
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements

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.

Problem

• Ensuring safety in robot navigation with disturbances
• Addressing noisy measurements in MPC design
• Synthesizing robust MPC from experimental data

Innovation

• Modular robust MPC design pipeline
• Data-driven disturbance bounds estimation
• Deterministic reproducible code synthesis