Harmonising Safety Paradigms: Energy-Aware Control of Active Response and Passive Compliance for Safety-Critical Robotic Tasks
Xinyuan Zhao, Wenyu Liang, Junyuan Xue, Yan Wu
AI summary
Problem
Active safety methods like Control Barrier Functions often become infeasible under dynamic obstacles or perception noise, while variable impedance control risks instability from unbounded energy injection, and existing energy tank recharging strategies lack adaptability without compromising passivity guarantees.
Approach
The framework merges CBF-based obstacle avoidance with fallback actions and passive compliance for impact mitigation, governed by an energy tank that safely recharges only at motion equilibrium to preserve system passivity.
Key results
- Unified active-passive safety framework with CBF fallback actions
- Novel task-agnostic energy tank recharging condition preserving passivity
- Kalman-filtered energy monitoring for rapid impact detection and compliance switching
- Hardware validation on KUKA iiwa 14 demonstrating robust dynamic obstacle avoidance and collision mitigation
Why it matters
Enables safer, more reliable human-robot collaboration by providing a theoretically sound, adaptable safety mechanism for real-world manipulation tasks.
Abstract
Ensuring safety in robotic manipulation is increas- ingly critical as robots become integrated into human-shared environments for complex physical interaction tasks. This paper presents an energy-aware control framework that combines ac- tive responses with passive compliance for safety-critical robotic manipulation. Specifically, Control Barrier Functions (CBFs) are employed for active collision avoidance with detected obstacles, which are then integrated with fallback safety actions to resolve potential violation of CBF constraints. Complementing this active safety paradigm, a passive safety paradigm is implemented to mitigate post-collision impacts by monitoring energy variance and limiting power exchanges. Furthermore, an energy tank is incorporated to enforce passivity of the robot, which is crucial to address potential instability issues in variable impedance control. To make the tank adaptive to varying energy requirements arising from dynamic environments and unpredictable events, we propose a novel, task-agnostic tank recharging condition without compromising the system’s passivity guarantee. The effectiveness of the proposed control framework is validated through experiments on a KUKA iiwa 14 robot.