Noccaro A, Cordella F, Zollo L, Di Pino G, Guglielmelli E, Formica D
Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction, Department of Medicine, Università Campus Bio-Medico, via Alvaro del Portillo 21, 00128, Rome, Italy.
Unit of Biomedical Robotics and Biomicrosystems, Department of Engineering, Università Campus Bio-Medico, via Alvaro del Portillo 21, 00128, Rome, Italy.
ROMAN. 2017 Aug 1;2017:156-161. doi: 10.1109/ROMAN.2017.8172295. Epub 2017 Dec 14.
In this paper we propose and validate a teleoperated control approach for an anthropomorphic redundant robotic manipulator, using magneto-inertial sensors (IMUs). The proposed method allows mapping the motion of the human arm (used as the master) on the robot end-effector (the slave). We record arm movements using IMU sensors, and calculate human forward kinematics to be mapped on robot movements. In order to solve robot kinematic redundancy, we implemented different algorithms for inverse kinematics that allows imposing anthropomorphism criteria on robot movements. The main objective is to let the user to control the robotic platform in an easy and intuitive manner by providing the control input freely moving his/her own arm and exploiting redundancy and anthropomorphism criteria in order to achieve human-like behaviour on the robot arm. Therefore, three inverse kinematics algorithms are implemented: Damped Least Squares (DLS), Elastic Potential (EP) and Augmented Jacobian (AJ). In order to evaluate the performance of the algorithms, four healthy subjects have been asked to control the motion of an anthropomorphic robot arm (i.e. the Kuka Light Weight Robot 4+) through four magneto-inertial sensors (i.e. Xsens Wireless Motion Tracking sensors - MTw) positioned on their arm. Anthropomorphism indices and position and orientation errors between the human hand pose and the robot end-effector pose were evaluated to assess the performance of our approach.
在本文中，我们提出并验证了一种用于拟人冗余机器人操纵器的遥操作控制方法，该方法使用磁惯性传感器（IMU）。所提出的方法允许将人类手臂（用作主设备）的运动映射到机器人末端执行器（从设备）上。我们使用IMU传感器记录手臂运动，并计算要映射到机器人运动上的人类正向运动学。为了解决机器人运动学冗余问题，我们实现了不同的逆运动学算法，这些算法允许在机器人运动上施加拟人标准。主要目标是让用户通过自由移动自己的手臂提供控制输入，并利用冗余和拟人标准，以在机器人手臂上实现类人行为，从而以简单直观的方式控制机器人平台。因此，实现了三种逆运动学算法：阻尼最小二乘法（DLS）、弹性势能法（EP）和增强雅可比法（AJ）。为了评估这些算法的性能，已要求四名健康受试者通过位于他们手臂上的四个磁惯性传感器（即Xsens无线运动跟踪传感器 - MTw）来控制拟人机器人手臂（即库卡轻型机器人4+）的运动。评估了拟人指标以及人类手部姿态与机器人末端执行器姿态之间的位置和方向误差，以评估我们方法的性能。
