IEEE Trans Cybern. 2022 Jul;52(7):6462-6475. doi: 10.1109/TCYB.2020.3043798. Epub 2022 Jul 4.
In this survey, various concepts and methodologies developed over the past two decades for varying and learning the impedance or admittance of robotic systems that physically interact with humans are explored. For this purpose, the assumptions and mathematical formulations for the online adjustment of impedance models and controllers for physical human-robot interaction (HRI) are categorized and compared. In this systematic review, studies on: 1) variation and 2) learning of appropriate impedance elements are taken into account. These strategies are classified and described in terms of their objectives, points of view (approaches), and signal requirements (including position, HRI force, and electromyography activity). Different methods involving linear/nonlinear analyses (e.g., optimal control design and nonlinear Lyapunov-based stability guarantee) and the Gaussian approximation algorithms (e.g., Gaussian mixture model-based and dynamic movement primitives-based strategies) are reviewed. Current challenges and research trends in physical HRI are finally discussed.
在这项调查中，探索了过去二十年来为物理上与人交互的机器人系统的阻抗或导纳而开发的各种概念和方法。为此，对物理人机交互（HRI）中阻抗模型和控制器的在线调整的假设和数学公式进行了分类和比较。在这个系统综述中，研究了：1）适当阻抗元素的变化和 2）学习。这些策略根据其目标、观点（方法）和信号要求（包括位置、HRI 力和肌电图活动）进行分类和描述。综述了涉及线性/非线性分析的不同方法（例如，最优控制设计和基于非线性 Lyapunov 的稳定性保证）和高斯近似算法（例如，基于高斯混合模型和基于动态运动基元的策略）。最后讨论了物理 HRI 中的当前挑战和研究趋势。
