Use of unmanned aerial vehicle (UAV) technology is predicted to increase dramatically from more
than 600,000 drones registered just with the US Federal Aviation Administration (FAA) to nearly
7,000,000 over the next 12 years ( FAA 1,2 ) This popularity is evident in their increasing use in
and around public outdoor spaces, including parks, stadiums, outdoor amphitheaters, festival
grounds, or outdoor markets. While there is considerable research on unmanned aerial vehicle
(UAV) applications and navigation (Koh 2012, Nemeth 2010) and an emerging body of work in
landscape architecture (Kullmann 2017, Park 2016), there is no research addressing increasing
conflicts between public space visitors, drone navigation in public space, and its effect on the
planning and design of public space. The paper presents initial findings from funded research to
develop landscape architectural design and planning responses supporting low cost detection
technology to deter the illegal use of drones in public spaces. Methods of data collection
employed surveys of botanical garden visitors concerning their preferences for site landscape
features and experiences, their awareness, attitudes, and preferences about the presence of
drones in public space, and potential aerial visual access to a range of forested and open
landscapes frequented by visitors in the garden. Findings suggest that given public concern
about the presence of drones, landscape planning and design of such public spaces should
provide continuous landscape features with restricted aerial visual access surrounding and
connecting public areas with open aerial visual access.
more »
« less
Using unmanned aerial vehicles to sample aquatic ecosystems: Unmanned aerial vehicles in limnology
- NSF-PAR ID:
- 10047940
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Limnology and Oceanography: Methods
- Volume:
- 15
- Issue:
- 12
- ISSN:
- 1541-5856
- Page Range / eLocation ID:
- 1021 to 1030
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
While there has been significant progress on statistical theories in the information community, there is a lack of studies in information-theoretic distributed resource allocation to maximize information gain. With advanced technologies of unmanned aerial vehicles (UAVs) in response to corresponding revised FAA regulations, this study focuses on developing a new framework for utilizing UAVs in incident management. As a result of new computing technologies, predictive decision-making studies have recently improved ERV allocations for a sequence of incidents; however, these ground-based operations do not simultaneously capture network-wide information. This study incorporates a real-time aerial view using UAVs with three key improvements. First, aerial observations update the status of the freeway shoulder, allowing an ERV to safely travel at full speed. Second, observing parameters of the congestion shockwave provides accurate measurements of the true impact of an incident. Third, real-time information can be gathered on the clearance progress of an incident scene. We automate UAV and ERV allocation while satisfying constraints between these vehicles using a distributed constraint optimization problem (DCOP) framework. To find the optimal assignment of vehicles, the proposed model is formulated and solved using the Max-Sum approach. The system utility convergence is presented for different scenarios of grid size, number of incidents, and number of vehicles. We also present the solution of our model using the Distributed Stochastic Algorithm (DSA). DSA with exploration heuristics outperformed the Max-Sum algorithm when probability threshold p=0.5 but degrades for higher values of p.more » « less
-
Unmanned Aerial Networks (UAVs) are prone to several cyber-attacks, including Global Positioning Spoofing attacks. For this purpose, numerous studies have been conducted to detect, classify, and mitigate these attacks, using Artificial Intelligence techniques; however, most of these studies provided techniques with low detection, high misdetection, and high bias rates. To fill this gap, in this paper, we propose three supervised deep learning techniques, namely Deep Neural Network, U Neural Network, and Long Short Term Memory. These models are evaluated in terms of Accuracy, Detection Rate, Misdetection Rate, False Alarm Rate, Training Time per Sample, Prediction Time, and Memory Size. The simulation results indicated that the U Neural Network outperforms other models with an accuracy of 98.80%, a probability of detection of 98.85%, a misdetection of 1.15%, a false alarm of 1.8%, a training time per sample of 0.22 seconds, a prediction time of 0.2 seconds, and a memory size of 199.87 MiB. In addition, these results depicted that the Long Short-Term Memory model provides the lowest performance among other models for detecting these attacks on UAVs.more » « less
-
null (Ed.)Schistosomiasis, or “snail fever”, is a parasitic disease affecting over 200 million people worldwide. People become infected when exposed to water containing particular species of freshwater snails. Habitats for such snails can be mapped using lightweight, inexpensive and field-deployable consumer-grade Unmanned Aerial Vehicles (UAVs), also known as drones. Drones can obtain imagery in remote areas with poor satellite imagery. An unexpected outcome of using drones is public engagement. Whereas sampling snails exposes field technicians to infection risk and might disturb locals who are also using the water site, drones are novel and fun to watch, attracting crowds that can be educated about the infection risk.more » « less
-
Testing and Evaluation of Radio Frequency Immunity of Unmanned Aerial Vehicles For Bridge InspectionRecent technological advances have led to an increase in the adoption of Unmanned Aerial Vehicles (UAVs) in a variety of use-case scenarios. In particular, Departments of Transportation in several states in the United States have been exploring the use of UAVs for bridge and infrastructure inspections to improve safety and reduce the costs of the inspection process. UAVs are remotely piloted from a cockpit or a ground station via radio channels. The UAV's state information and payload information are also transmitted to the cockpit/ground station via radio frequency (RF) signals. The RF channels that are commonly used by most UAVs are 72-73, 902-928 and 2400-2483.5 MHz bands, which is also shared by several other communication protocols such as, WiFi and ZigBee networks, and therefore, the interference effects with the other services on the UAV's operation performance cannot be overlooked, particularly to maintain the minimum distance from the close by surfaces while flying alongside and underneath the bridges to achieve the best results. The loss of signal or even signal strength during such close flights can cause damage to the UAV. Especially while inspecting the bridges located in the urban areas that involve a lot of RF communication around due to presence of sever RC devices providing different services. Conventional Electromagnetic Compatibility (EMC) adherence requirements imposed on electronic systems are not adequate for UAVs due to their airborne nature and the presence of the other RF sources in the environment. Thus, in this work, we investigate the compliance of EMC requirements by designing and conducting field experiments to expose the UAVs to electromagnetic interference and distortions that are likely to be encountered during the UAV operation. The results of this work will enable us to assess the level of RF immunity of the general-purpose UAVs to aid in the selection of a suitable UAV platform for bridge inspection and develop safety procedures for minimizing the impact of RF interference.more » « less
