论文标题
使用基于SEMG的力预测对脆弱和可变形物体的高精度指导的高精度抓地力控制
Force-guided High-precision Grasping Control of Fragile and Deformable Objects using sEMG-based Force Prediction
论文作者
论文摘要
高精度调节接触力对于抓握和操纵脆弱或可变形物体至关重要。我们旨在利用人类手的敏捷性来调节机器人手的接触力，并以可穿戴和无创的方式利用人类感觉运动协同作用。我们通过表面肌电图（SEMG）在骨骼肌的自愿性收缩期间从骨骼肌的电活动中提取了力信息。我们建立了一个基于神经网络的回归模型，以预测预处理的SEMG信号的吸引力并达到高精度（R2 = 0.982）。基于人类肌肉预测的力命令，我们开发了一个力引导的控制框架，在该框架中，通过录取控制器实现了力控制，该控制器追踪了预测的抓紧力引用以抓住精致且可变形的物体。我们证明了所提出的方法在日常生活中的一组代表性脆弱和可变形的物体上的有效性，所有这些物体都成功地抓住了而没有任何损坏或变形。
Regulating contact forces with high precision is crucial for grasping and manipulating fragile or deformable objects. We aim to utilize the dexterity of human hands to regulate the contact forces for robotic hands and exploit human sensory-motor synergies in a wearable and non-invasive way. We extracted force information from the electric activities of skeletal muscles during their voluntary contractions through surface electromyography (sEMG). We built a regression model based on a Neural Network to predict the gripping force from the preprocessed sEMG signals and achieved high accuracy (R2 = 0.982). Based on the force command predicted from human muscles, we developed a force-guided control framework, where force control was realized via an admittance controller that tracked the predicted gripping force reference to grasp delicate and deformable objects. We demonstrated the effectiveness of the proposed method on a set of representative fragile and deformable objects from daily life, all of which were successfully grasped without any damage or deformation.
