Abstract
Human-like behavior has emerged in the robotics area for improving the quality of Human-Robot Interaction (HRI). For the human-like behavior imitation, the kinematic mapping between a human arm and robot manipulator is one of the popular solutions. To fulfill this requirement, a reconstruction method called swivel motion was adopted to achieve human-like imitation. This approach aims at modeling the regression relationship between robot pose and swivel motion angle. Then it reaches the human-like swivel motion using its redundant degrees of the manipulator. This characteristic holds for most of the redundant anthropomorphic robots. Although artificial neural network (ANN) based approaches show moderate robustness, the predictive performance is limited. In this paper, we propose a novel deep convolutional neural network (DCNN) structure for reconstruction enhancement and reducing online prediction time. Finally, we utilized the trained DCNN model for managing redundancy control a 7 DoFs anthropomorphic robot arm (LWR4+, KUKA, Germany) for validation. A demonstration is presented to show the human-like behavior on the anthropomorphic manipulator. The proposed approach can also be applied to control other anthropomorphic robot manipulators in industry area or biomedical engineering.
Highlights
Hman-like has attracted increasing research interest in the past decades in various areas, such as industrial application, service robot, and medical sector, etc
This paper presents a novel nonlinear regression algorithm to map the relation between swivel angle and the hand pose using deep neural network (DNN) approach to improve the ability of nonlinear regression analysis of human-like motion model
WORK This paper proposed a novel deep convolutional neural network structure (DCNN) in human-like redundancy optimization for anthropomorphic manipulators
Summary
Hman-like has attracted increasing research interest in the past decades in various areas, such as industrial application, service robot, and medical sector, etc. In particular for the cases that robots and humans share the workspace [1], [2], for example, close collaboration between human operators and industrial robots manufacturing, assistance system for elderly users, etc., Human-Robot Interaction(HRI) plays a vital role in these practical applications [3], [4]. It has been proven that both humanoid appearances and human-like motion behavior can facilitate task performance of HRI [5]. The anthropomorphic serial manipulators [5], for example, LWR4+ (KUKA, Augsburg, Germany), Justin robot (Institute of Robotics and Mechatronics, Wessling, Germany) and YuMi (ABB, Zurich, Switzerland), with a similar mechanical structure with human arm, a human-like behavior of the robot arm pose can be an enhancement of this topic because it provides a more social and reasonable movement in HRI [6]. A reaching task with the human-like motion for robot-environment interactions
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