Inverse Kinematics Based Human Mimicking System using Skeletal Tracking Technology

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• 作者：Mina Alibeigi ; Sadegh Rabiee…
• 关键词：Imitation learning ; Mimicry ; Human ; robot interaction ; Humanoid robot ; Nao humanoid robot ; Microsoft kinect sensor
• 刊名：Journal of Intelligent & Robotic Systems
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：85
• 期：1
• 页码：27-45
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Control, Robotics, Mechatronics; Electrical Engineering; Artificial Intelligence (incl. Robotics); Mechanical Engineering;
• 出版者：Springer Netherlands
• ISSN：1573-0409
• 卷排序：85

文摘

Humanoid robots needs to have human-like motions and appearance in order to be well-accepted by humans. Mimicking is a fast and user-friendly way to teach them human-like motions. However, direct assignment of observed human motions to robot’s joints is not possible due to their physical differences. This paper presents a real-time inverse kinematics based human mimicking system to map human upper limbs motions to robot’s joints safely and smoothly. It considers both main definitions of motion similarity, between end-effector motions and between angular configurations. Microsoft Kinect sensor is used for natural perceiving of human motions. Additional constraints are proposed and solved in the projected null space of the Jacobian matrix. They consider not only the workspace and the valid motion ranges of the robot’s joints to avoid self-collisions, but also the similarity between the end-effector motions and the angular configurations to bring highly human-like motions to the robot. Performance of the proposed human mimicking system is quantitatively and qualitatively assessed and compared with the state-of-the-art methods in a human-robot interaction task using Nao humanoid robot. The results confirm applicability and ability of the proposed human mimicking system to properly mimic various human motions.