用分层二次规划让机器人与人互动时既安全又灵活。
Control Barrier Functions Solved with Hierarchical Quadratic Programming for Safe Physical Human-Robot Interaction
- 基于分层二次规划设计安全与性能任务的协同框架。
- 实验证明该方法在冗余机器人上可稳定运行且支持多任务平衡。
- 适合需要高安全性的人机协作场景，如康复机器人。
物理人机交互有望结合人类智能与机器人物理能力，实现如康复协作等应用。安全是这类系统部署的关键。近年来，控制屏障函数（CBF）成为确保安全性的有效方法，已广泛应用于自适应巡航和足式机器人导航等领域。CBF可通过二次规划（QP）求解，支持多种形式的安全任务。为管理大量安全任务，分层CBF允许任务分级松弛，保障冲突情况下的可行性。本文提出将基于CBF的分层二次规划（HQP）框架应用于人机交互，可在任意层级同时设计性能任务（如保持交互点的理想行为）与安全任务，更灵活地平衡安全性与性能。在一台真实冗余机器人上进行了大量实验，验证了该方法的有效性、灵活性与通用性。
原文摘要 · Abstract (English)
Physical human-robot interaction offers the potential to leverage human intelligence and robot physical capabilities to enable a range of exciting applications, e.g., collaborative robots for rehabilitation. Safety is critical for the successful deployment of this kind of robotic system. In recent years, Control Barrier Function (CBF) has emerged as an effective approach to enforce safety guarantees, which has been widely applied in various applications, from adaptive cruise control to navigation of legged robots. CBFs can be solved in a Quadratic Programming (QP) problem, which can include many CBF-formulated tasks. To manage a large number of safety tasks, a hierarchical CBF has been used to allow hierarchical relaxation of safety tasks to ensure the feasibility of a solution in the presence of conflicting tasks. In this work, we propose to use a CBF-based Hierarchical Quadratic Programming (HQP) framework in physical human-robot interaction to allow us to design both performance tasks (e.g., preserve the desired behavior at the human-robot interaction point) and safety tasks at any level of a hierarchy to balance the safety and the performance in a more flexible way. Extensive experiments were carried out on a real redundant robot to validate the effectiveness, flexibility, and generality of this approach.
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