A teleoperated control approach for anthropomorphic manipulator using magneto-inertial sensors.
A Noccaro, F Cordella, L Zollo, G Di Pino, E Guglielmelli, D Formica
Author Information
A Noccaro: Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction, Department of Medicine, Università Campus Bio-Medico, via Alvaro del Portillo 21, 00128, Rome, Italy.
F Cordella: Unit of Biomedical Robotics and Biomicrosystems, Department of Engineering, Università Campus Bio-Medico, via Alvaro del Portillo 21, 00128, Rome, Italy.
L Zollo: Unit of Biomedical Robotics and Biomicrosystems, Department of Engineering, Università Campus Bio-Medico, via Alvaro del Portillo 21, 00128, Rome, Italy.
G Di Pino: Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction, Department of Medicine, Università Campus Bio-Medico, via Alvaro del Portillo 21, 00128, Rome, Italy.
E Guglielmelli: Unit of Biomedical Robotics and Biomicrosystems, Department of Engineering, Università Campus Bio-Medico, via Alvaro del Portillo 21, 00128, Rome, Italy.
D Formica: Unit of Biomedical Robotics and Biomicrosystems, Department of Engineering and with the Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction, Department of Medicine, Università Campus Bio-Medico di Roma, via Alvaro del Portillo 21, 00128, Rome, Italy.
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.
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