用模型预测控制与哈密顿-雅可比方法，让自主系统既高效又安全。
Safe and Performant Deployment of Autonomous Systems via Model Predictive Control and Hamilton-Jacobi Reachability Analysis
- 结合MPC与HJ可达性分析，动态优化任务性能
- 在4D杜宾车和6自由度机械臂上验证，安全约束满足率显著提升
- 适用于对安全与效率要求高的自动驾驶、机器人场景
尽管自主系统算法已取得显著进展，能执行复杂任务，但实现高效且安全的运行仍具挑战。现有方法或无法提供安全保证，或为安全牺牲任务性能。本文提出一种基于模型预测控制（MPC）与哈密顿-雅可比（HJ）可达性分析的框架，在保障安全约束的同时优化任务性能。该框架确保了MPC控制器的递归可行性，并可扩展至高维系统。通过两个仿真案例验证：使用4维杜宾车和6自由度Kuka iiwa机械臂，结果表明，相比基线方法，本框架显著提升了系统对安全约束的满足程度。
原文摘要 · Abstract (English)
While we have made significant algorithmic developments to enable autonomous systems to perform sophisticated tasks, it remains difficult for them to perform tasks effective and safely. Most existing approaches either fail to provide any safety assurances or substantially compromise task performance for safety. In this work, we develop a framework, based on model predictive control (MPC) and Hamilton-Jacobi (HJ) reachability, to optimize task performance for autonomous systems while respecting the safety constraints. Our framework guarantees recursive feasibility for the MPC controller, and it is scalable to high-dimensional systems. We demonstrate the effectiveness of our framework with two simulation studies using a 4D Dubins Car and a 6 Dof Kuka iiwa manipulator, and the experiments show that our framework significantly improves the safety constraints satisfaction of the systems over the baselines.
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