Design and Evaluation of Anthropomorphic Robotic Hand for Object Grasping and Shape Recognition
Rahul Raj Devaraja, Rytis Maskeliūnas, Robertas Damaševičius
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%.
Topics & Concepts
Artificial intelligenceComputer visionRobotic handComputer scienceObject (grammar)Support vector machineClassifier (UML)Cognitive neuroscience of visual object recognitionRobotic armPattern recognition (psychology)RobotRobot Manipulation and LearningSoft Robotics and ApplicationsRobotics and Sensor-Based Localization