基于实测数据构建鲁棒移动机器人导航控制，提升真实场景安全性。
From Data to Safe Mobile Robot Navigation: An Efficient and Modular Robust MPC Design Pipeline
- 用闭环实验数据迭代估计扰动范围，避免理想化假设。
- 在四旋翼仿真中实现约束始终满足与递归可行性。
- 提供可复现代码，适合关注安全控制的工程研究者。
模型预测控制（MPC）是自主移动机器人导航中规划与控制的强大策略。然而，在实际部署中，由于存在扰动和测量噪声，保障安全性仍具挑战。现有方法常依赖理想化假设，忽视测量噪声影响，并随意猜测不切实际的边界。本文提出一种高效、模块化的鲁棒MPC设计流程，系统性解决上述问题。该流程采用迭代方法，利用闭环实验数据估计扰动边界，并合成鲁棒输出反馈MPC方案。我们以确定性和可复现的代码形式提供了该流程，支持从数据生成鲁棒输出反馈MPC。在Gazebo仿真中，通过四旋翼平台验证了其鲁棒约束满足性与递归可行性。
原文摘要 · Abstract (English)
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.
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