Abstract
This paper presents an adaptive neural network learning-based solution for the inverse kinematics of humanoid fingers. For the purpose, we specify an effective finger model by considering the interphalangeal joint coordination inherent in human fingers. In order to find a proper joint combination for any fingertip trajectory, we propose an adaptive learning scheme by using a multi-layered neural network. It is interesting to use an adaptive learning rate algorithm that leads the neural network to get the inverse kinematic solution quickly. The usefulness of the proposed approach is verified by exemplary simulations for the general motion of humanoid fingers.
Highlights
In order to manipulate the fingers of a robotic hand, we need to know the combination of joints of each finger [1][2]
The objective of this paper is to provide an adaptive learning-based method to get the inverse kinematic solution of humanoid fingers with a coupling between the distal interphalangeal joint and the proximal interphalangeal joint
In order to verify the usefulness of the adaptive neural network learning scheme, we utilized an effective model of the human fingers and performed exemplary simulations for the inverse kinematics of the index and middle fingers, where a four-layered neural network has been employed
Summary
In order to manipulate the fingers of a robotic hand, we need to know the combination of joints of each finger [1][2]. It is not easy to obtain an effective joint configuration due to the redundancy or constraints To solve this issue, some approaches have been proposed [3,4,5]. Yoshikawa [3] and Chiu [4] suggested a performance index-based algorithm using a manipulability criterion and a compatibility index, respectively, from the viewpoint of finding an effective posture of robot manipulators These methods have an advantage with regard to resolving the singularity posture of a manipulator as well as avoiding obstacles. Secco’s method gives a simple closed-form solution, but it has a limitation with regard to implementing the realistic movement of human fingers This is because the third joint of a humanoid finger should actuate identically with the second joint through his approach. Since the method does not consider the phalangeal length parameters, this may lead to difficulties in making a consistent grasp configuration
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