Safe and Performant Deployment of Autonomous Systems via Model Predictive Control and Hamilton-Jacobi Reachability Analysis

2025-06-29

AI Summary

Addressing the challenge of simultaneously ensuring safety and task performance for autonomous systems in complex scenarios, this paper proposes a synergistic framework integrating Model Predictive Control (MPC) with Hamilton–Jacobi (HJ) reachability analysis. The method employs online HJ-based computation of safety constraint sets, which are embedded into the MPC optimization problem to guarantee recursive feasibility and satisfaction of safety boundaries—even in high-dimensional state spaces—while preserving MPC’s explicit task-objective optimization capability. Compared to conventional safety-augmented MPC or standalone reachability approaches, our framework achieves a +23.6% improvement in safety constraint satisfaction rate in simulations of a 4D Dubins car and a 6-DOF KUKA iiwa manipulator, with only marginal degradation in task performance and strong scalability to higher dimensions. The core innovation lies in the tightly coupled modeling of safety verification and performance optimization.

Technology Category

• Natural Language Processing: Safety and Robustness
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Ensuring autonomous systems operate safely and effectively
• Balancing task performance with safety constraints
• Scalable framework for high-dimensional autonomous systems

Innovation

• Combines MPC and HJ reachability for safety
• Guarantees recursive feasibility in MPC
• Scalable to high-dimensional autonomous systems