Gentili Rodolphe J, Oh Hyuk, Molina Javier, Reggia James A, Contreras-Vidal José L
Department of Kinesiology, School of Public Health, Maryland Robotics Center, Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD 20742, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:3052-5. doi: 10.1109/EMBC.2012.6346608.
In order to approach human hand performance levels, artificial anthropomorphic hands/fingers have increasingly incorporated human biomechanical features. However, the performance of finger reaching movements to visual targets involving the complex kinematics of multi-jointed, anthropomorphic actuators is a difficult problem. This is because the relationship between sensory and motor coordinates is highly nonlinear, and also often includes mechanical coupling of the two last joints. Recently, we developed a cortical model that learns the inverse kinematics of a simulated anthropomorphic finger. Here, we expand this previous work by assessing if this cortical model is able to learn the inverse kinematics for an actual anthropomorphic humanoid finger having its two last joints coupled and controlled by pneumatic muscles. The findings revealed that single 3D reaching movements, as well as more complex patterns of motion of the humanoid finger, were accurately and robustly performed by this cortical model while producing kinematics comparable to those of humans. This work contributes to the development of a bioinspired controller providing adaptive, robust and flexible control of dexterous robotic and prosthetic hands.
为了达到人类手部的性能水平，人造拟人化手/手指越来越多地融入了人类生物力学特征。然而，涉及多关节拟人化致动器复杂运动学的手指向视觉目标的伸展运动性能是一个难题。这是因为感觉坐标和运动坐标之间的关系高度非线性，并且通常还包括最后两个关节的机械耦合。最近，我们开发了一个学习模拟拟人化手指逆运动学的皮层模型。在此，我们通过评估这个皮层模型是否能够学习由气动肌肉耦合和控制最后两个关节的实际拟人化人形手指的逆运动学来扩展之前的工作。研究结果表明，这个皮层模型能够准确且稳健地执行单个三维伸展运动以及人形手指更复杂的运动模式，同时产生与人类相当的运动学。这项工作有助于开发一种受生物启发的控制器，为灵巧的机器人手和假肢手提供自适应、稳健且灵活的控制。
