Abstract
We developed an anthropomorphic multi-finger artificial hand for a fine-scale object grasping task, sensing the grasped object’s shape. The robotic hand was created using the 3D printer and has the servo bed for stand-alone finger movement. The data containing the robotic fingers’ angular position are acquired using the Leap Motion device, and a hybrid Support Vector Machine (SVM) classifier is used for object shape identification. We trained the designed robotic hand on a few monotonous convex-shaped items similar to everyday objects (ball, cylinder, and rectangular box) using supervised learning techniques. We achieve the mean accuracy of object shape recognition of 94.4%.
Highlights
This article aimed to develop and evaluate a robotic hand, which can identify the shape of the item it is holding, for custom grasping tasks
We can consider the robotic arm as a spatial mechanism; the number of degrees of freedom (DoF) is 27
The robotic robotic hand hand architecture architecture used used in in this this study study isisbased based on onknown knownrobotic robotichand hand prototypes prototypes with with aa pulley-tendon pulley-tendon transmission transmission with with fingers, fingers, which which are are moved moved by byserial serial kinematic kinematic chains chains with with revolute revolutejoints joints(e.g., (e.g.,see see[37])
Summary
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