A bio-inspired grasp optimization algorithm for an anthropomorphic robotic hand

A fundamental requirement for assistive robots is to guarantee a safe and human-like way to perform their tasks. In particular, the ability to realize smooth movements and obtain a stable grasp is of primary importance. In this perspective, this paper aims at studying human grasping and developing a bio-inspired method for power-grip posture prediction and finger trajectory planning for a robotic hand. The developed method is based on neuroscientific assumptions and experimental evidence coming from the observation of the human behavior during power grip. It is based on the minimization of a suitably defined function to identify the optimal grasp configuration and the choice of a logarithmic spiral trajectory for moving the fingers. The behavior of ten different subjects during the grasping action has been analyzed with the CyberGlove motion capture data glove. A common thumb posture has been observed and has been introduced in the grasping algorithm. The algorithm performance has been tested on an anthropomorphic robotic hand by means of simulation trials. The results demonstrate the effectiveness of the approach and pave the way for the implementation on a real robotic hand.