An official website of the United States government
With recent changes by the Federal Aviation Administration (FAA) opening the possibility of more areas for drones to be used, such as delivery, there will be increasingly more intera ctions between humans and drones soon. Although current human drone interaction (HDI) investigate what factors are necessary for safe interactions, very few has focused on drone illumination. Therefore, in this study, we explored how illumination affects users’ perception of the drone through a distance perception task. Data analysis did not indicate any significant effects in the normal distance estimation task for illumination or distance conditions. However, most participants underestimated the distance in the normal distance estimation task and indicated that the LED drone was closer when it wa s illuminated during the relative distance estimation task, even though the drones were equidistant. In future studies, factors such as the weather conditions, lighting patterns, and height of the drone will be explored.
more »
« less
Two Supervised Machine Learning Approaches for Wind Velocity Estimation Using Multi-Rotor Copter Attitude Measurements
In this work we address the adequacy of two machine learning methods to tackle the problem of wind velocity estimation in the lowermost region of the atmosphere using on-board inertial drone data within an outdoor setting. We fed these data, and accompanying wind tower measurements, into a K-nearest neighbor (KNN) algorithm and a long short-term memory (LSTM) neural network to predict future windspeeds, by exploiting the stabilization response of two hovering drones in a wind field. Of the two approaches, we found that LSTM proved to be the most capable supervised learning model during more capricious wind conditions, and made competent windspeed predictions with an average root mean square error of 0.61 m·s−1 averaged across two drones, when trained on at least 20 min of flight data. During calmer conditions, a linear regression model demonstrated acceptable performance, but under more variable wind regimes the LSTM performed considerably better than the linear model, and generally comparable to more sophisticated methods. Our approach departs from other multi-rotor-based windspeed estimation schemes by circumventing the use of complex and specific dynamic models, to instead directly learn the relationship between drone attitude and fluctuating windspeeds. This exhibits utility in a range of otherwise prohibitive environments, like mountainous terrain or off-shore sites.
more »
« less
- Award ID(s):
- 1810762
- PAR ID:
- 10273945
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 20
- Issue:
- 19
- ISSN:
- 1424-8220
- Page Range / eLocation ID:
- 5638
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Although consumer drones have been used in many attacks, besides specific methods such as jamming, very little research has been conducted on systematical methods to counter these drones. In this paper, we develop generic methods to compromise drone position control algorithms in order to make malicious drones deviate from their targets. Taking advantage of existing methods to remotely manipulate drone sensors through cyber or physical attacks (e.g., [1], [2]), we exploited the weaknesses of position estimation and autopilot controller algorithms on consumer drones in the proposed attacks. For compromising drone position control, we first designed two state estimation attacks: a maximum False Data Injection (FDI) attack and a generic FDI attack that compromised the Kalman-Filter-based position estimation (arguably the most popular method). Furthermore, based on the above attacks, we proposed two attacks on autopilot-based navigation, to compromise the actual position of a malicious drone. To the best of our knowledge, this is the first piece of work in this area. Our analysis and simulation results show that the proposed attacks can significantly affect the position estimation and the actual positions of drones. We also proposed potential countermeasures to address these attacks.more » « less
-
IEEE (Ed.)This research involves developing a drone control system that functions by relating EEG and EMG from the forehead to different facial movements using recurrent neural networks (RNN) such as long-short term memory (LSTM) and gated recurrent Unit (GRU). As current drone control methods are largely limited to handheld devices, regular operators are actively engaged while flying and cannot perform any passive control. Passive control of drones would prove advantageous in various applications as drone operators can focus on additional tasks. The advantages of the chosen methods and those of some alternative system designs are discussed. For this research, EEG signals were acquired at three frontal cortex locations (fp1, fpz , fp2 ) using electrodes from an OpenBCI headband and observed for patterns of Fast Fourier Transform (FFT) frequency-amplitude distributions. Five different facial expressions were repeated while recording EEG signals of 0-60Hz frequencies with two reference electrodes placed on both earlobes. EMG noise received during EEG measurements was not filtered away but was observed to be minimal. A dataset was first created for the actions done, and later categorized by a mean average error (MAE), a statistical error deviation analysis and then classified with both an LSTM and GRU neural network by relating FFT amplitudes to the actions. On average, the LSTM network had classification accuracy of 78.6%, and the GRU network had a classification accuracy of 81.8%.more » « less
-
Drone simulators are often used to reduce training costs and prepare operators for various ad-hoc scenarios, as well as to test the quality of algorithmic and communication aspects in collaborative scenarios. An important aspect of drone missions in simulated (as well as real life) environments is the operational lifetime of a given drone, in both solo and collaborative fleet settings. Its importance stems from the fact that the capacity of the on-board batteries in untethered (i.e., free-flying) drones determines the range and/or the length of the trajectory that a drone can travel in the course of its surveilance or delivery missions. Most of the existing simulators incorporate some kind of a consumption model based on different parameters of the drone and its flight trajectory. However, to our knowledge, the existing simulators are not capable of incorporating data obtained from actual physical measurements/observations into the consumption model. In this work, we take a first step towards enabling the (users of) drones simulator to incorporate the speed and direction of the wind into the model and monitor its impact on the battery consumption as the direction of the flight changes relative to the wind. We have also developed a proof-of-concept implementation with DJI Mavic 3 and Parrot ANAFI drones.more » « less
-
he pervasive operation of customer drones, or small-scale unmanned aerial vehicles (UAVs), has raised serious concerns about their privacy threats to the public. In recent years, privacy invasion events caused by customer drones have been frequently reported. Given such a fact, timely detection of invading drones has become an emerging task. Existing solutions using active radar, video or acoustic sensors are usually too costly (especially for individuals) or exhibit various constraints (e.g., requiring visual line of sight). Recent research on drone detection with passive RF signals provides an opportunity for low-cost deployment of drone detectors on commodity wireless devices. However, the state of the arts in this direction rely on line-of-sight (LOS) RF signals, which makes them only work under very constrained conditions. The support of more common scenarios, i.e., non-line-of-sight (NLOS), is still missing for low-cost solutions. In this paper, we propose a novel detection system for privacy invasion caused by customer drone. Our system is featured with accurate NLOS detection with low-cost hardware (under $50). By exploring and validating the relationship between drone motions and RF signal under the NLOS condition, we find that RF signatures of drones are somewhat “amplified” by multipaths in NLOS. Based on this observation, we design a two-step solution which first classifies received RSS measurements into LOS and NLOS categories; deep learning is then used to extract the signatures and ultimately detect the drones. Our experimental results show that LOS and NLOS signals can be identified at accuracy rates of 98.4% and 96% respectively. Our drone detection rate for NLOS condition is above 97% with a system implemented using Raspberry PI 3 B+.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s20195638
