Publication
Teaching a drone to accompany a person from demonstrations using non-linear ASFM
Conference Article
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Edition
2019
Pages
1985-1991
Doc link
https://doi.org/10.1109/IROS40897.2019.8967675
File
Authors
Projects associated
Abstract
In this paper, we present a new method based on the Aerial Social Force Model (ASFM) to allow human-drone side-by-side social navigation in real environments. To tackle this problem, the present work proposes a new nonlinear-based approach using Neural Networks. To learn and test the rightness of the new approach, we built a new dataset with simulated environments and we recorded motion controls provided by a human expert tele-operating the drone. The recorded data is then used to train a neural network which maps interaction forces to acceleration commands. The system is also reinforced with a human path prediction module to improve the drone’s navigation, as well as, a collision detection module to completely avoid possible impacts. Moreover, a performance metric is defined which allows us to numerically evaluate and compare the fulfillment of the different learned policies. The method was validated by a large set of simulations; we also conducted real-life experiments with an autonomous drone to verify the framework described for the navigation process. In addition, a user study has been realized to reveal the social acceptability of the method.
Categories
mobile robots, service robots.
Author keywords
Aerial Robot, Learning by demonstration
Scientific reference
A. Garrell Zulueta, C. Coll, R. Alquézar Mancho and A. Sanfeliu. Teaching a drone to accompany a person from demonstrations using non-linear ASFM, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2019, Macau, China, pp. 1985-1991.
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