| dc.description.abstract | This study aims to use the inertial measurement unit sensor (IMU) data to reconstruct the human skeleton animation posture in Unity. A self-designed motion capture system is used to record the time series trajectory data of human animation. In this system, human controls Nao-V6 remotely by human posture with motion retargeting method, and gets the robot vision by VR headset, making user have an immersive experience. Design an audio system to communicate with the user of operator side and robot side.
Equipped with the smooth default foot movement in Nao V6 due to the feedback information of the sensors that mounted on the robot and Linear Inverse Pendulum model. Considering the safety of the robot, the foot control such as move forward, move sideway and turn action will be triggered by threshold. People always use different gestures to meet with various requirement of daily life, that is the reason that gesture control must be more sophisticated. Two different motion retargeting methods are indicated and compared in this research, inverse kinematics and reinforcement learning.
The inverse kinematics method needs to build Denavit-Hartenberg parameter model for each robot’s arm, and map the Cartesian coordinate of the current human posture to the robot dimension. The joint angles of the robot will be back-calculated through the current human arm position by the inverse kinematics solution. The reinforcement learning adopts Actor-Critic network. For the model learning, the robot should make six pre-designed motions. In the training phase, the human gesture will generate the gesture of the robot, the model parameter will update by reward and punishment rules. The proposed system has been demonstrated to successfully recognize subjects’ different in the initial onset of each motion action. According to the analysis of the average Fréchet distance, the average trajectory error of the system is 1.9 cm, and it has a high stability in the motion retargeting control. | en_US |