Úbeda, A.; Zapata-Impata, B.S.; Puente, S.T.; Gil, P.; Candelas, F.; Torres, F. A Vision-Driven Collaborative Robotic Grasping System Tele-Operated by Surface Electromyography. Sensors2018, 18, 2366.
Úbeda, A.; Zapata-Impata, B.S.; Puente, S.T.; Gil, P.; Candelas, F.; Torres, F. A Vision-Driven Collaborative Robotic Grasping System Tele-Operated by Surface Electromyography. Sensors 2018, 18, 2366.
This paper presents a system that merges computer vision and surface electromyography techniques to carry out grasping tasks. To perform this, the vision-driven system is used to compute pre-grasping poses of the robotic system based on the analysis of tridimensional object features. Then, the human operator can correct the pre-grasping pose of the robot using surface electromyographic signals from the forearm during wrist flexion and extension. Weak wrist flexions and extensions allow a fine adjustment of the robotic system to grasp the object and finally, when the operator considers that the grasping position is optimal, a strong flexion is performed to initiate the grasping of the object. The system has been tested with several subjects to check its performance showing a grasping accuracy of around 95% of the attempted grasps which increases by around a 9% the grasping accuracy of previous experiments in which electromyographic control was not implemented.
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