用肌腱位移和张力估算机械手关节角度，实现无传感器闭环控制。
Tendon-based modelling, estimation and control for a simulated high-DoF anthropomorphic hand model
- 基于D-H参数建立手部运动学模型，通过非线性优化求解肌腱状态与关节角关系。
- 在MuJoCo仿真中成功实现五指每指5自由度、拇指6自由度的手部动作跟踪。
- 适合研究无传感肌腱驱动机械手的控制与建模，尤其关注人形机器人手设计。
肌腱驱动的人形机械手常因缺乏直接关节角度传感而受限，集成关节编码器会牺牲机械紧凑性和灵活性。本文提出一种从测量的肌腱位移和张力估计关节位置的计算方法。首先基于Denavit-Hartenberg（D-H）约定构建人形手部高效运动学建模框架；采用简化肌腱模型，推导出肌腱状态与关节位置之间的非线性方程组，并通过非线性优化求解。所估关节角用于基于雅可比矩阵的比例-积分（PI）控制器，结合前馈项实现闭环控制，无需直接关节传感即可完成手势跟踪。该估计与控制框架的有效性及局限性在MuJoCo仿真环境中得到验证，采用的是具有每根长指5个自由度、拇指6个自由度的解剖学精确生物机电手模型。
原文摘要 · Abstract (English)
Tendon-driven anthropomorphic robotic hands often lack direct joint angle sensing, as the integration of joint encoders can compromise mechanical compactness and dexterity. This paper presents a computational method for estimating joint positions from measured tendon displacements and tensions. An efficient kinematic modeling framework for anthropomorphic hands is first introduced based on the Denavit-Hartenberg convention. Using a simplified tendon model, a system of nonlinear equations relating tendon states to joint positions is derived and solved via a nonlinear optimization approach. The estimated joint angles are then employed for closed-loop control through a Jacobian-based proportional-integral (PI) controller augmented with a feedforward term, enabling gesture tracking without direct joint sensing. The effectiveness and limitations of the proposed estimation and control framework are demonstrated in the MuJoCo simulation environment using the Anatomically Correct Biomechatronic Hand, featuring five degrees of freedom for each long finger and six degrees of freedom for the thumb.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。