Design of Dynamics Invariant LSTM for Touch Based Human-UAV Interaction Detection
release_oo5rmv7f6bgvbfolt2zlqfvrna
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Anees Peringal, Mohamad Chehadeh, Rana Azzam, Mahmoud Hamandi, Igor Boiko, Yahya Zweiri
2022
Abstract
The field of Unmanned Aerial Vehicles (UAVs) has reached a high level of
maturity in the last few years. Hence, bringing such platforms from closed
labs, to day-to-day interactions with humans is important for commercialization
of UAVs. One particular human-UAV scenario of interest for this paper is the
payload handover scheme, where a UAV hands over a payload to a human upon their
request. In this scope, this paper presents a novel real-time human-UAV
interaction detection approach, where Long short-term memory (LSTM) based
neural network is developed to detect state profiles resulting from human
interaction dynamics. A novel data pre-processing technique is presented; this
technique leverages estimated process parameters of training and testing UAVs
to build dynamics invariant testing data. The proposed detection algorithm is
lightweight and thus can be deployed in real-time using off the shelf UAV
platforms; in addition, it depends solely on inertial and position measurements
present on any classical UAV platform. The proposed approach is demonstrated on
a payload handover task between multirotor UAVs and humans. Training and
testing data were collected using real-time experiments. The detection approach
has achieved an accuracy of 96\%, giving no false positives even in the
presence of external wind disturbances, and when deployed and tested on two
different UAVs.
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