Data-driven body-machine interface for the accurate control of drones.
- Miehlbradt J Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, 1202 Geneva, Switzerland.
- Cherpillod A Laboratory of Intelligent Systems, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
- Mintchev S Laboratory of Intelligent Systems, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
- Coscia M Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, 1202 Geneva, Switzerland.
- Artoni F Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, 1202 Geneva, Switzerland.
- Floreano D Laboratory of Intelligent Systems, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
- Micera S Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, 1202 Geneva, Switzerland; silvestro.micera@epfl.ch.
- 2018-07-18
Published in:
- Proceedings of the National Academy of Sciences of the United States of America. - 2018
English
The accurate teleoperation of robotic devices requires simple, yet intuitive and reliable control interfaces. However, current human-machine interfaces (HMIs) often fail to fulfill these characteristics, leading to systems requiring an intensive practice to reach a sufficient operation expertise. Here, we present a systematic methodology to identify the spontaneous gesture-based interaction strategies of naive individuals with a distant device, and to exploit this information to develop a data-driven body-machine interface (BoMI) to efficiently control this device. We applied this approach to the specific case of drone steering and derived a simple control method relying on upper-body motion. The identified BoMI allowed participants with no prior experience to rapidly master the control of both simulated and real drones, outperforming joystick users, and comparing with the control ability reached by participants using the bird-like flight simulator Birdly.
- Language
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- English
- Open access status
- hybrid
- Identifiers
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- DOI 10.1073/pnas.1718648115
- PMID 30012599
- Persistent URL
- https://sonar.rero.ch/global/documents/112103
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