Gentili Rodolphe J, Oh Hyuk, Molina Javier, Contreras-Vidal José L
Department of Kinesiology and the Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD 20742, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:8251-4. doi: 10.1109/IEMBS.2011.6092034.
The performance of reaching movements to visual targets requires complex kinematic mechanisms such as redundant, multijointed, anthropomorphic actuators and thus is a difficult problem since the relationship between sensory and motor coordinates is highly nonlinear. In this article, we present a neural model able to learn the inverse kinematics of a simulated anthropomorphic robot finger (ShadowHand™ finger) having four degrees of freedom while performing 3D reaching movements. The results revealed that this neural model was able to control accurately and robustly the finger when performing single 3D reaching movements as well as more complex patterns of motion while generating kinematics comparable to those observed in human. The long term goal of this research is to design a bio-mimetic controller providing adaptive, robust and flexible control of dexterous robotic/prosthetics hands.
向视觉目标进行伸手动作的执行需要复杂的运动学机制，例如冗余的、多关节的、拟人化的执行器，因此这是一个难题，因为感觉坐标和运动坐标之间的关系高度非线性。在本文中，我们提出了一种神经模型，该模型能够在执行三维伸手动作时学习具有四个自由度的模拟拟人机器人手指（ShadowHand™手指）的逆运动学。结果表明，该神经模型在执行单个三维伸手动作以及更复杂的运动模式时，能够准确且稳健地控制手指，同时生成与人类观察到的运动学相当的运动学。这项研究的长期目标是设计一种仿生控制器，为灵巧的机器人/假肢手提供自适应、稳健且灵活的控制。
