In this article, a novel human-machine interface, in which two Leap Motion (LM) controllers and a coil are attached to a Cartesian platform to provide contactless electromagnetic force feedback for enhancing the accuracy and efficiency of human-robot manipulation tasks is presented. To implement such an interface, an interval Kalman filter, an improved particle filter, and a mean filter are integrated to estimate accurately the position and orientation of the hand gesture tracked by the two LM controllers, and to smoothen the movement of the Cartesian platform. The back propagation neural network is employed to regulate the electric currents of the coil attached to the Cartesian platform for accurate force feedback. A series of comparative experiments are performed, and the results show that the presented interface greatly improved the efficiency and accuracy of human-robot manipulation tasks in comparison with existing methods, indicating its great potentials for many industry scenarios.

Additional Metadata
Keywords Cartesian platform, contactless electromagnetic force feedback, markerless vision-based gesture tracking, robot manipulation interaction
Persistent URL dx.doi.org/10.1109/TII.2020.2966756
Journal IEEE Transactions on Industrial Informatics
Citation
Du, G. (Guanglong), Zhang, B. (Bo), Li, C. (Chunquan), Gao, B. (Boyu), & Liu, P. (2020). Natural Human-Machine Interface with Gesture Tracking and Cartesian Platform for Contactless Electromagnetic Force Feedback. IEEE Transactions on Industrial Informatics, 16(11), 6868–6879. doi:10.1109/TII.2020.2966756