階層的二次計画法で解く制御バリア関数による安全な物理的ヒューマンロボットインタラクション
Control Barrier Functions Solved with Hierarchical Quadratic Programming for Safe Physical Human-Robot Interaction
物理的ヒューマンロボットインタラクションにおける安全性を確保するため、制御バリア関数を階層的二次計画法で解く枠組みを提案し、実機冗長ロボットで実験検証した。
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著者: Rui Luo, Jonas Mariager Jakobsen, Wesley Roozing, Federico Califano, Cheng Fang
分類: cs.RO
原文アブストラクト
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.
