Concept: Unmanned Aircraft System
Regulations have allowed for increased unmanned aircraft systems (UAS) operations over the last decade, yet operations over people are still not permitted. The objective of this study was to estimate the range of injury risks to humans due to UAS impact. Three commercially-available UAS models that varied in mass (1.2-11 kg) were evaluated to estimate the range of risk associated with UAS-human interaction. Live flight and falling impact tests were conducted using an instrumented Hybrid III test dummy. On average, live flight tests were observed to be less severe than falling impact tests. The maximum risk of AIS 3+ injury associated with live flight tests was 11.6%, while several falling impact tests estimated risks exceeding 50%. Risk of injury was observed to increase with increasing UAS mass, and the larger models tested are not safe for operations over people in their current form. However, there is likely a subset of smaller UAS models that are safe to operate over people. Further, designs which redirect the UAS away from the head or deform upon impact transfer less energy and generate lower risk. These data represent a necessary impact testing foundation for future UAS regulations on operations over people.
Estimating animal populations is critical for wildlife management. Aerial surveys are used for generating population estimates, but can be hampered by cost, logistical complexity, and human risk. Additionally, human counts of organisms in aerial imagery can be tedious and subjective. Automated approaches show promise, but can be constrained by long setup times and difficulty discriminating animals in aggregations. We combine unmanned aircraft systems (UAS), thermal imagery and computer vision to improve traditional wildlife survey methods. During spring 2015, we flew fixed-wing UAS equipped with thermal sensors, imaging two grey seal (Halichoerus grypus) breeding colonies in eastern Canada. Human analysts counted and classified individual seals in imagery manually. Concurrently, an automated classification and detection algorithm discriminated seals based upon temperature, size, and shape of thermal signatures. Automated counts were within 95-98% of human estimates; at Saddle Island, the model estimated 894 seals compared to analyst counts of 913, and at Hay Island estimated 2188 seals compared to analysts' 2311. The algorithm improves upon shortcomings of computer vision by effectively recognizing seals in aggregations while keeping model setup time minimal. Our study illustrates how UAS, thermal imagery, and automated detection can be combined to efficiently collect population data critical to wildlife management.
The use of small Unmanned Aircraft Systems (UAS; also known as “drones”) for professional and personal-leisure use is increasing enormously. UAS operate at low altitudes (<500 m) and in any terrain, thus they are susceptible to interact with local fauna, generating a new type of anthropogenic disturbance that has not been systematically evaluated. To address this gap, we performed a review of the existent literature about animals' responses to UAS flights and conducted a pooled analysis of the data to determine the probability and intensity of the disturbance, and to identify the factors influencing animals' reactions towards the small aircraft. We found that wildlife reactions depended on both the UAS attributes (flight pattern, engine type and size of aircraft) and the characteristics of animals themselves (type of animal, life-history stage and level of aggregation). Target-oriented flight patterns, larger UAS sizes, and fuel-powered (noisier) engines evoked the strongest reactions in wildlife. Animals during the non-breeding period and in large groups were more likely to show behavioral reactions to UAS, and birds are more prone to react than other taxa. We discuss the implications of these results in the context of wildlife disturbance and suggest guidelines for conservationists, users and manufacturers to minimize the impact of UAS. In addition, we propose that the legal framework needs to be adapted so that appropriate actions can be undertaken when wildlife is negatively affected by these emergent practices.
The use of a UAS (Unmanned Aircraft System) was tested to survey large mammals in the Nazinga Game Ranch in the south of Burkina Faso. The Gatewing ×100™ equipped with a Ricoh GR III camera was used to test animal reaction as the UAS passed, and visibility on the images. No reaction was recorded as the UAS passed at a height of 100 m. Observations, made on a set of more than 7000 images, revealed that only elephants (Loxodonta africana) were easily visible while medium and small sized mammals were not. The easy observation of elephants allows experts to enumerate them on images acquired at a height of 100 m. We, therefore, implemented an aerial strip sample count along transects used for the annual wildlife foot count. A total of 34 elephants were recorded on 4 transects, each overflown twice. The elephant density was estimated at 2.47 elephants/km(2) with a coefficient of variation (CV%) of 36.10%. The main drawback of our UAS was its low autonomy (45 min). Increased endurance of small UAS is required to replace manned aircraft survey of large areas (about 1000 km of transect per day vs 40 km for our UAS). The monitoring strategy should be adapted according to the sampling plan. Also, the UAS is as expensive as a second-hand light aircraft. However the logistic and flight implementation are easier, the running costs are lower and its use is safer. Technological evolution will make civil UAS more efficient, allowing them to compete with light aircraft for aerial wildlife surveys.
This study explores the potential use of drones in searching for and locating victims and of motorized transportation of search and rescue providers in a mountain environment using a simulation model.
Unmanned aircraft systems (UAS), colloquially called drones, are used commonly for military, government, and civilian purposes, including both commercial and consumer applications. During a search and rescue mission in Oregon, a UAS was used to confirm a fatality in a slot canyon; this eliminated the need for a dangerous rappel at night by rescue personnel. A second search mission in Oregon used several UAS to clear terrain. This allowed search of areas that were not accessible or were difficult to clear by ground personnel. UAS with cameras may be useful for searching, observing, and documenting missions. It is possible that UAS might be useful for delivering equipment in difficult areas and in communication.
Avian nests are frequently concealed or camouflaged, but a number of species builds noticeable nests or use conspicuous materials for nest decoration. In most cases, nest decoration has a role in mate choice or provides thermoregulatory or antiparasitic benefits. In territorial species however, decorations may serve additional or complementary functions, such as extended phenotypic signaling of nest-site occupancy and social status to potential intruders. The latter may benefit both signaler and receiver by minimizing the risk of aggressive interactions, especially in organisms with dangerous weaponry. Support for this hypothesis was recently found in a population of black kites (Milvus migrans), a territorial raptor that decorates its nest with white artificial materials. However, the crucial assumption that nest decorations increased nest-site visibility to conspecifics was not assessed, a key aspect given that black kite nests may be well concealed within the canopy. Here, we used an unmanned aircraft system to take pictures of black kite nests, with and without an experimentally placed decoration, from different altitudes and distances simulating the perspective of a flying and approaching, prospecting intruder. The pictures were shown to human volunteers through a standardized routine to determine whether detection rates varied according the nest decoration status and distance. Decorated nests consistently showed a higher detection frequency and a lower detection-latency, compared to undecorated versions of the same nests. Our results confirm that nest decoration in this species may act as a signaling medium that enhances nest visibility for aerial receivers, even at large distances. This finding complements previous work on this communication system, which showed that nest decoration was a threat informing trespassing conspecifics on the social dominance, territory quality and fighting capabilities of the signaler.
Many high-risk plant pathogens are transported over long distances (hundreds of meters to thousands of kilometers) in the atmosphere. The ability to track the movement of these pathogens in the atmosphere is essential for forecasting disease spread and establishing effective quarantine measures. Here, we discuss the scales of atmospheric dispersal of plant pathogens along a transport continuum (pathogen scale, farm scale, regional scale, and continental scale). Growers can use risk information at each of these dispersal scales to assist in making plant disease management decisions, such as the timely application of appropriate pesticides. Regional- and continental-scale atmospheric features known as Lagrangian coherent structures (LCSs) may shuffle plant pathogens along highways in the sky. A promising new method relying on overlapping turbulent back-trajectories of pathogen-laden parcels of air may assist in localizing potential inoculum sources, informing local and/or regional management efforts such as conservation tillage. The emergence of unmanned aircraft systems (UASs, or drones) to sample plant pathogens in the lower atmosphere, coupled with source localization efforts, could aid in mitigating the spread of high-risk plant pathogens. Expected final online publication date for the Annual Review of Phytopathology Volume 53 is August 04, 2015. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.
This paper presents an unmanned aircraft system (UAS) that uses a probabilistic model for autonomous front-on environmental sensing or photography of a target. The system is based on low-cost and readily-available sensor systems in dynamic environments and with the general intent of improving the capabilities of dynamic waypoint-based navigation systems for a low-cost UAS. The behavioural dynamics of target movement for the design of a Kalman filter and Markov model-based prediction algorithm are included. Geometrical concepts and the Haversine formula are applied to the maximum likelihood case in order to make a prediction regarding a future state of a target, thus delivering a new waypoint for autonomous navigation. The results of the application to aerial filming with low-cost UAS are presented, achieving the desired goal of maintained front-on perspective without significant constraint to the route or pace of target movement.
Inspired by sensing strategies observed in birds and bats, a new attitude control concept of directly using real-time pressure and shear stresses has recently been studied. It was shown that with an array of onboard airflow sensors, small unmanned aircraft systems can promptly respond to airflow changes and improve flight performances. In this paper, a mapping function is proposed to compute aerodynamic moments from the real-time pressure and shear data in a practical and computationally tractable formulation. Since many microscale airflow sensors are embedded on the small unmanned aircraft system surface, it is highly possible that certain sensors may fail. Here, an adaptive control system is developed that is robust to sensor failure as well as other numerical mismatches in calculating real-time aerodynamic moments. The advantages of the proposed method are shown in the following simulation cases: (i) feedback pressure and wall shear data from a distributed array of 45 airflow sensors; (ii) 50% failure of the symmetrically distributed airflow sensor array; and (iii) failure of all the airflow sensors on one wing. It is shown that even if 50% of the airflow sensors have failures, the aircraft is still stable and able to track the attitude commands.