METHOD OF CLASSIFYING GRASPS BY ROBOT HANDS

This research characterizes grasping by multifingered robot hands through investiga- tion of the space of contact forces into four subspaces , a method is developed to determine the di- mensions of the subspaces with respect to the connectivity of the object. The relationship reveals the differences between three types of grasps classified and indicates how the contact force can be decomposed corresponding to each type of grasp. The subspaces and the determination of their di- mensions are illlustrated by examples.

Enhance Egocentric Grasp Recognition Based Flex Sensor Under Low Illumination

Egocentric recognition is exciting computer vision research by acquiring images and video from the first-person overview.However,an image becomes noisy and dark under low illumination conditions,making subsequent hand detection tasks difficult.Thus,image enhancement is necessary to make buried detail more visible.This article addresses the challenge of egocentric hand grasp recognition in low light conditions by utilizing the flex sensor and image enhancement algorithm based on adaptive gamma correction with weighting distribution.Initially,a flex sensor is installed to the thumb for object manipulation.The thumb placement that holds in a different position on the object of each grasp affects the voltage changing of the flex sensor circuit.The average voltages are used to configure the weighting parameter to improve images in the image enhancement stage.Moreover,the contrast and gamma function are used to adjust varies the low light condition.These grasp images are then separated to be training and testing with pretrained deep neural networks as the feature extractor in YOLOv2 detection network for the grasp recognition system.The proposed of using a flex sensor significantly improves the grasp recognition rate in low light conditions.

Variations in the Biomechanics of 16 Palmar Hand Regions Related to Tomato Picking

The aim of this study is to systematically reveal the differences in the biomechanics of 16 hand regions related to bionic picking of tomatoes.The biomechanical properties(peak loading force,elastic coefficient,maximum percentage deformation and interaction contact mechanics between human hand and tomato fruit)of each hand region were experimentally measured and covariance analyzed.The results revealed that there were significant variations in the assessed biomechanical properties between the 16 hand regions(p<0.05).The maximum pain force threshold(peak loading force in I2 region)was 5.11 times higher than the minimum pain force threshold(in Th1 region).It was found that each hand region in its normal direction can elastically deform by at least 15.30%.The elastic coefficient of the 16 hand regions ranged from 0.22 to 2.29 N mm−1.The interaction contact force acting on the fruit surface was affected by the selected human factors and fruit features.The obtained covariance models can quantitatively predict all of the above biomechanical properties of 16 hand regions.The findings were closely related to hand grasping performance during tomato picking,such as soft contact,surface interaction,stable and dexterous grasping,provided a foundation for developing a high-performance tomato-picking bionic robotic hand.