具有本体感知柔顺控制的仿生腱驱 DexHand 021 的开发
Development of the Bioinspired Tendon-Driven DexHand 021 with Proprioceptive Compliance Control
AI总结：
本文开发了重1kg、具有19个自由度的腱驱五指灵巧手DexHand 021，提出基于本体感知力感知的导纳控制方法，实现高精度力估计与柔顺操作，成功完成33种GRASP抓取动作。
摘要：
人手在日常生活和工业应用中发挥着至关重要的作用，然而在机器人系统中复现其多功能能力——包括运动、感知和协调操作——仍然是一项艰巨的挑战。开发灵巧机器人手需要在类人灵活性与工程约束（如复杂性、尺寸重量比、耐用性和力感知性能）之间取得平衡。本文介绍了 DexHand 021，一款高性能、线缆驱动的五指机器人手，具有 12 个主动自由度和 7 个被动自由度（DoFs），在轻量化 1 kg 设计中实现了 19 个自由度的灵巧性。我们提出了一种基于本体感知力感知的导纳控制方法来增强操作能力。实验结果表明其优越性能：单指负载能力超过 10 N，指尖重复定位精度低于 0.001 m，力估计误差低于 0.2 N。与 PID 控制相比，多物体抓取中的关节力矩降低了 31.19%，显著提升了力感知能力，同时防止碰撞过程中的过载。该手在强力抓取和精确抓取方面均表现出色，成功执行了 33 种 GRASP 分类动作和复杂操作任务。这项工作推进了轻量化、工业级灵巧手的设计，并增强了本体感知控制，为机器人操作和智能制造做出了贡献。
英文摘要：
The human hand plays a vital role in daily life and industrial applications, yet replicating its multifunctional capabilities-including motion, sensing, and coordinated manipulation with robotic systems remains a formidable challenge. Developing a dexterous robotic hand requires balancing human-like agility with engineering constraints such as complexity, size-to-weight ratio, durability, and force-sensing performance. This letter presents Dex-Hand 021, a high-performance, cable-driven five-finger robotic hand with 12 active and 7 passive degrees of freedom (DoFs), achieving 19 DoFs dexterity in a lightweight 1 kg design. We propose a proprioceptive force-sensing-based admittance control method to enhance manipulation. Experimental results demonstrate its superior performance: a single-finger load capacity exceeding 10 N, fingertip repeatability under 0.001 m, and force estimation errors below 0.2 N. Compared to PID control, joint torques in multi-object grasping are reduced by 31.19%, significantly improves force-sensing capability while preventing overload during collisions. The hand excels in both power and precision grasps, successfully executing 33 GRASP taxonomy motions and complex manipulation tasks. This work advances the design of lightweight, industrial-grade dexterous hands and enhances proprioceptive control, contributing to robotic manipulation and intelligent manufacturing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
