Salvietti Gionata
Department of Information Engineering and Mathematics, University of Siena, Siena, Italy.
Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy.
Front Neurorobot. 2018 Jun 7;12:27. doi: 10.3389/fnbot.2018.00027. eCollection 2018.
This review reports the principal solutions proposed in the literature to reduce the complexity of the control and of the design of robotic hands taking inspiration from the organization of the human brain. Several studies in neuroscience concerning the sensorimotor organization of the human hand proved that, despite the complexity of the hand, a few parameters can describe most of the variance in the patterns of configurations and movements. In other words, humans exploit a reduced set of parameters, known in the literature as synergies, to control their hands. In robotics, this dimensionality reduction can be achieved by coupling some of the degrees of freedom (DoFs) of the robotic hand, that results in a reduction of the needed inputs. Such coupling can be obtained at the software level, exploiting mapping algorithm to reproduce human hand organization, and at the hardware level, through either rigid or compliant physical couplings between the joints of the robotic hand. This paper reviews the main solutions proposed for both the approaches.
本综述报告了文献中提出的主要解决方案,这些方案旨在从人类大脑的组织中汲取灵感,降低机器人手控制和设计的复杂性。神经科学中关于人类手部感觉运动组织的多项研究证明,尽管手部结构复杂,但少数参数就能描述其构型和运动模式中的大部分变化。换句话说,人类利用文献中称为协同作用的一组简化参数来控制手部。在机器人技术中,这种降维可以通过耦合机器人手的一些自由度(DoF)来实现,这将减少所需的输入。这种耦合可以在软件层面通过利用映射算法来重现人类手部组织来实现,也可以在硬件层面通过机器人手关节之间的刚性或柔性物理耦合来实现。本文综述了针对这两种方法提出的主要解决方案。