Online human-like redundancy optimization for tele-operated anthropomorphic manipulators. (3rd December 2018)

• Record Type: Journal Article
• Authors: Su, Hang; Enayati, Nima; Vantadori, Luca; Spinoglio, Andrea; Ferrigno, Giancarlo; De Momi, Elena
• Is Part Of: International journal of advanced robotic systems. Volume 15:Number 6(2018:Nov./Dec.)
• Publication Date: 2018-12-03
• Subjects: Human-like motion -- neural network -- robot redundancy optimization -- tele-operation -- vision feedback; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881418814695
• Languages: English
• ISSNs: 1729-8806

Abstract

Robot human-like behavior can enhance the performance of human–robot cooperation with prominently improved natural interaction. This also holds for redundant robots with an anthropomorphic kinematics. In this article, we translated human ability of managing redundancy to control a seven degrees of freedom anthropomorphic robot arm (LWR4+, KUKA, Germany) during tele-operated tasks. We implemented a nonlinear regression method—based on neural networks—between the human arm elbow swivel angle and the hand target pose to achieve an anthropomorphic arm posture during tele-operation tasks. The method was assessed in simulation and experiments were performed with virtual reality tracking tasks in a lab environment. The results showed that the robot achieves a human-like arm posture during tele-operation, and the subjects prefer to work with the biologically inspired robot. The proposed method can be applied in control of anthropomorphic robot manipulators for tele-operated collaborative tasks, such as in factories or in operating rooms.