Drones or unoccupied aerial vehicles are rapidly being used for a spectrum of applications, including replacing traditional occupied aircraft as a means of approaching wildlife from the air. Though less intrusive to wildlife than occupied aircraft, drones can still cause varying levels of disturbance. Policies and protocols to guide lowest-impact drone flights are most likely to succeed if considerations are derived from knowledge from scientific literature. This study examines trends in the scientific literature on using drones to approach wildlife between 2000 and 2020, specifically in relation to the publication types, scientific journals that works are published in, purposes of drone flights reported, taxa studied, and locations of studies. From 223 publications, we observed a large increase in relevant scientific literature, the majority of which were peer-reviewed articles published across 86 scientific journals. The largest proportion of peer-reviewed research articles related to aquatic mammals or aquatic birds and the use or trial of drone flights for conducting population surveys, animal detection, or investigations of animal responses to drone flights. The largest proportion of articles were studies conducted in North America and Australia. Since animal responses to drone flights vary among taxa, populations, and geographic locations, we encourage further growth in the volume of relevant scientific literature needed to inform policies and protocols for specific taxa and (or) locations, particularly where knowledge gaps exist.
This study seeks to contribute to the literature by presenting a discussion of potential cyber risks and precautionary measures concerning unmanned vehicles as a whole. In this study, Global Navigation Satellite System (GNSS) spoofing, jamming, password cracking, denial-of-service (DoS), injecting malware, and modification of firmware are identified as potential cyberattack methods against unmanned vehicles. Potential deterrents against the aforementioned cyberattack methods are suggested as well. Illustrations of such safeguards include creating an architecture of the multi-agent system, using solid-state storage components, applying distributed programming tools and techniques, implementing sophisticated encryption techniques for data storage and transmission, deploying additional sensors and systems, and comparing the data received from different sensors.
Aerial imagery surveys are commonly used in marine mammal research to determine population size, distribution and habitat use. Analysis of aerial photos involves hours of manually identifying individuals present in each image and converting raw counts into useable biological statistics. Our research proposes the use of deep learning algorithms to increase the efficiency of the marine mammal research workflow. To test the feasibility of this proposal, the existing YOLOv4 convolutional neural network model was trained to detect belugas, kayaks and motorized boats in oblique drone imagery, collected from a stationary tethered system. Automated computer-based object detection achieved the following precision and recall, respectively, for each class: beluga = 74%/72%; boat = 97%/99%; and kayak = 96%/96%. We then tested the performance of computer vision tracking of belugas and occupied watercraft in drone videos using the DeepSORT tracking algorithm, which achieved a multiple-object tracking accuracy (MOTA) ranging from 37% to 88% and multiple object tracking precision (MOTP) between 63% and 86%. Results from this research indicate that deep learning technology can detect and track features more consistently than human annotators, allowing for larger datasets to be processed within a fraction of the time while avoiding discrepancies introduced by labeling fatigue or multiple human annotators.
Interest in advanced air mobility (AAM) and urban air mobility (UAM) operations for on-demand passenger and cargo transport continues to grow. There is ongoing research on market demand and forecast, community acceptance, privacy, and security. There is also ongoing research by National Aeronautics and Space Administration , Federal Aviation Administration, academia, and industry on airspace integration, regulatory, process, and procedural challenges. Safe integration of UAM and AAM will also require different stakeholder perspectives such as air traffic controllers, manned aircraft pilots, remote pilots, UAM operators, and the community. This research aimed to assess the willingness of manned aircraft pilots to operate in UAM integrated airspace based on airspace complexity and UAM automation level. In addition, a moderated mediation analysis was conducted using trust and perceived risk as mediators and operator type as a moderating variable. The results indicated that automation level influenced pilots’ willingness to operate an aircraft in integrated airspace. A moderating effect of operation type on automation level and willingness to pilot an aircraft was also observed: professional pilots were more amenable to UAM operations with a pilot on board compared with remotely piloted operations. Results from the study are expected to inform airspace integration challenges, processes, and procedures for UAM integrated operations.
Climate-induced sea-ice loss represents the greatest threat to polar bears (Ursus maritimus Phipps, 1774), and utilizing drones to characterize behavioural responses to sea-ice loss is valuable for forecasting polar bear persistence. In this manuscript, we review previously published literature and draw on our own experience of using multirotor aerial drones to study polar bear behaviour in the Canadian Arctic. Specifically, we suggest that drones can minimize human–bear conflicts by allowing users to observe bears from a safe vantage point; produce high-quality behavioural data that can be reviewed as many times as needed and shared with multiple stakeholders; and foster knowledge generation through co-production with northern communities. We posit that in some instances drones may be considered as an alternative tool for studying polar bear foraging behaviour, interspecific interactions, human–bear interactions, human safety and conflict mitigation, and den-site location at individual-level small spatial scales. Finally, we discuss flying techniques to ensure ethical operation around polar bears, regulatory requirements to consider, and recommend that future research focus on understanding polar bears’ behavioural and physiological responses to drones and the efficacy of drones as a deterrent tool for safety purposes.
Remotely piloted aircraft systems (RPASs) are tools for military organizations to help remove humans from dangerous situations and permit operations in severe and inhospitable environments. To support the procurement of an RPAS fleet under Canada’s Strong, Secure, Engaged 2017 defence policy, the Royal Canadian Air Force (RCAF) under the RCAF Joint Unmanned Surveillance and Target Acquisition System project (subsequently replaced by the RCAF RPAS project) funded Defence Research and Development Canada – Toronto Research Centre to conduct a preliminary investigation of human factors (HF) issues relating to the performance of the crew in the ground control station (GCS) to control a RPAS. This paper presents a review of the RCAF research program conducted between 2014 and 2017 that discusses HF issues in RPAS operations and how training is associated with the HF attributes of decision-making, skills and knowledge, and mission preparation. Also, this paper presents a training needs analysis methodology and analysis that identified essential RPAS crew competencies (expressed as the knowledge, skills, and abilities required by each crew member to perform their respective tasks). Finally, this paper discusses work that investigated experimentation and evaluation capabilities to support RPAS operator training and GCS airworthiness certification.
Effective management of animal populations requires knowledge of life history parameters and estimates of population abundance. One method commonly used to estimate abundance is capture–recapture analyses of photographs. Small, relatively inexpensive, rotary-wing drones have become an effective platform for obtaining high-quality aerial photographs of whales. To conduct capture–recapture analyses the animal needs to be defined as marked or unmarked and the photographs must be of high quality. While a system for scoring quality and markedness has previously been developed for bowhead whales (Balaena mysticetus Linnaeus, 1758) ( Rugh et al. 1998 . Rep. int. Whal. Commn. 48: 501–512), a revised scoring system was needed to incorporate increased information in photographs taken by drones. We present a revised scoring system that enlarges two of the previously defined areas of the whale examined for markings and incorporates smaller markings into the definition of marked whales. We scored 30 whales using the previous criteria and the revised criteria developed in this paper. More whales were identified as marked (23%) and mark scores were higher for 30% of the zones scored using the new system. Increasing the number of marked whales during capture–recapture studies increases the precision of estimated parameters and permits us to make those estimates with smaller samples of photographs.
This paper describes a method by which the degree of autonomy of a system can be quantified in a manner that allows comparison between systems. The methodology revisits, refines, and extends the contextual autonomous capability (CAC) model proposed by the National Institute of Science and Technology (NIST) by defining three orthogonal system metrics against which the performance of a system may be assessed. During the development of this model, it was recognized that there existed two different but coupled domains of autonomy- the Executive Autonomy describing the degree of independence of a system during the execution of the mission; and the Developmental Autonomy describing the degree of independence of the system during preparation for the mission. The resulting methodology is explicitly developed to be system agnostic such that it could be applied to humans as well as computerized systems. As such, it provides a means of quantifiably comparing the performance of any two systems - including human and computer - that are performing comparable sets of missions. The proposed model is called the system-agnostic quantification of autonomy levels (SQuAL) model.
In this study, we aim at developing ways to directly translate raw drone data into actionable insights, thus enabling us to make management decisions directly from drone data. Drone photogrammetric data and data analytics were used to model stand-level immediate tending need and cost in regeneration forests. Field reference data were used to train and validate a logistic model for the binary classification of immediate tending need and a multiple linear regression model to predict the cost to perform the tending operation. The performance of the models derived from drone data was compared to models utilizing the following alternative data sources: airborne laser scanning data (ALS), prior information from forest management plans (Prior) and the combination of drone +Prior and ALS +Prior. The use of drone data and prior information outperformed the remaining alternatives in terms of classification of tending needs, whereas drone data alone resulted in the most accurate cost models. Our results are encouraging for further use of drones in the operational management of regeneration forests and show that drone data and data analytics are useful for deriving actionable insights.
A quadcopter manipulator system is an aerial robot consisting of a quadcopter with a robotic arm attached to it. The system has coupled nonlinear dynamics with uncertain time-varying parameters. The work in this paper focuses on designing an adaptive nonlinear controller to facilitate the uncertain system’s trajectory tracking and stability. The novelty of the proposed work is the design and implementation of an adaptive feedback linearization controller, called adaptive augmented torque (AAT) control, for the aerial robot. The control law is based on a feedback linearization controller with model reference adaptive controller and a tracking error-based augmented term. Using the input-to-state stability concept, a bound on the parameter estimation error is also developed. In the presented methodology, the controller uses estimated values of system parameters obtained from the adaptive mechanism and the tracking error to compute the control input using the AAT control law. An adaptive law for estimating unknown parameters is obtained using the strictly positive real-Lyapunov method. The asymptotic stability of the closed-loop system is analyzed via the Lyapunov theory. Simulations implemented in MATLAB and ROS/Gazebo and preliminary hardware experiments are presented to validate the theoretical results and to corroborate the performance of the AAT control law.
Drones may be valuable in polar research because they can minimize researcher activity and overcome logistical, financial, and safety obstacles associated with wildlife research in polar regions. Because polar species may be particularly sensitive to disturbance and some research suggests behavioral responses to drones are species-specific, there is a need for focal species-specific disturbance assessments. We evaluated behavioral responses of nesting Common Eiders (Somateria mollissima (Linnaeus, 1758), n = 19 incubating females) to first, second, or in a few cases third exposure of fixed-wing drone surveys using nest cameras. We found no effect of drone flights (F [1,23] = 0, P = 1.0) or previous exposures (F [1,23] = 0.75, P = 0.397) on the probability of a daily recess event (bird leaves nests). Drone flights did not impact recess length (F [1,25] = 1.34, P = 0.26); however, Common Eiders with prior drone exposure took longer recess events (F [1,25] = 5.27, P = 0.03). We did not observe any overhead vigilance behaviors common in other species while the drone was in the air, which may reflect Common Eiders’ anti-predator strategies of reducing activity at nests in response to aerial predators. Surveying nesting Common Eider colonies with a fixed-wing drone did not result in biologically meaningful behavioral changes, providing a potential tool for research and monitoring this polar nesting species.
Geotechnologies have significant potential for application in socio-environmental analysis coupled to disaster risk reduction. Equipment and applications are available that are supported by scientific computing, promoting advances in the acquisition and processing of remote sensing data. Among these are two types: (i) the associated equipment to technology LiDAR (light detection and ranging) and (ii) remotely piloted aircraft systems (RPAS) with platforms of remote sensors. Recently, an growing number of studies has been observed that have the potential for applications in the sensors equipped in RPAS for environmental studies, especially those that evaluate the impacts of natural disasters. In this context, the aim of this research is to demonstrate the possibilities of RPAS applications in the collection of data of interest in the management of natural disasters. Also associated with this task is the prospect of implementing some techniques of scientific computing necessary for the implementation of applications. With these activities, we seek to contribute to the advancement of the employment of RPAS in managing and preventing the risk of natural disasters.
We present a deep neural-net-based controller trained by a model-free reinforcement learning (RL) algorithm to achieve hover stabilization for a quadrotor unmanned aerial vehicle (UAV). With RL, two neural nets are trained. One neural net is used as a stochastic controller, which gives the distribution of control inputs. The other maps the UAV state to a scalar, which estimates the reward of the controller. A proximal policy optimization (PPO) method, which is an actor–critic policy gradient approach, is used to train the neural nets. Simulation results show that the trained controller achieves a comparable level of performance to a manually tuned proportional-derivative (PD) controller, despite not depending on any model information. The paper considers different choices of reward function and their influence on controller performance.
Study of bird microhabitat use is time consuming and labour intensive. Our objective was to present a proof of concept of how emerging, high-resolution bird survey methods can be combined with vegetation data collected via unmanned aerial vehicles to accurately and efficiently quantify bird microhabitat. We used sound localization to determine mourning warbler (Geothlypis philadelphia) song posts, and a hybrid light detection and ranging/digital aerial photogrammetry canopy height model to demonstrate how mourning warblers use regenerating vegetation on reclaimed well sites. We identified differences in vegetation heights at locations used by mourning warblers versus random background locations on a reclaimed well site, with sound localization and the canopy height model both providing measurements with 1-m resolution (t = −3.45, p = 0.002). These technologies have the potential to provide large numbers of accurate bird locations that can be associated with high-resolution, spatially explicit vegetation metrics and used in different ecological niche modeling frameworks.
Unmanned boats have gained a lot of interest in the maritime community during the last decade. Most hydrodynamic platforms employed for unmanned boats are based on traditional relatively simple hulls. In the present study, small-scale hydrofoil-assisted unmanned boats (0.6–0.7 m in length and 3.5–5.5 kg in mass) have been developed and tested. Design calculations using a hydrodynamic transverse-strip engineering method with semi-empirical correlations were applied to determine suitable dimensions for hydrofoil systems. The boat hulls and hydrofoils were fabricated by laying up carbon-fiber cloth sheets on foam cores or 3-D printed profiles. The boats were instrumented with outboard propulsors and electronic equipment for operations in both remote control and autopilot modes. In addition, an in-situ thrust-measuring module was designed and installed at the hull sterns to gather thrust data at GPS-measured speeds in the range between 0 and 11 m/s. The developed boats proved to be robust platforms capable of going over 600 m distances at high speeds while autonomously following preset paths. The presented methods and results can assist engineers developing unmanned surface vehicles that utilize advanced hydrodynamic concepts.
Unmanned aerial vehicles (UAVs) are established, valuable tools for wildlife surveys in marine and terrestrial environments; however, they are seldom utilized in freshwater ecosystems. Therefore, baseline data on the use of UAVs in lotic environments are needed that balances flight parameters (e.g., altitude and noise level) with image quality, while minimizing disturbance to individuals. Moreover, the traditional high-cost UAVs may present challenges to researchers conducting rapid assessments on species presence with limited funding. However, emerging, affordable UAV systems can provide this preliminary data to researchers, albeit with caveats on reliability of data. We tested a low-cost UAV system to document freshwater turtle presence, species distribution, and habitat use in a small North Carolina wetland. We observed minimal instances of turtles fleeing basking sites (∼0.7%), as this UAV system was only ∼2.1 dB above ambient noise levels at an altitude of 20 m. Freshwater turtles were found primarily in algal mat basking habitats with highly variable numbers observed across locations and flights, likely due to image quality reliability and altitude. Our affordable UAV system was successful in providing baseline information on species presence, size distribution, and habitat preference of turtles in freshwater ecosystems.
To understand how a forest may respond to environmental changes or develop over time, it is necessary to examine broad, landscape level factors. With the arrival of unmanned aerial vehicles (UAVs), the combination of both spaceborne data with high resolution UAV data can provide foresters and biologists with powerful tools to classify canopies to the species level, which we illustrate here. We combine imagery from the Operational Land Imager (OLI) of the Landsat 8 satellite with aerial imagery from a Phantom 4 UAV to map canopy composition of three tree species. We manually delineated dense stands of each tree species in the UAV imagery to extract training samples from an OLI true colour composite image to perform a fuzzy membership analysis and calculate the maximum likelihood that an individual pixel represented a particular species. We verified the accuracy of our analysis finding an overall accuracy of 0.796 and a Kappa statistic of 0.728. We consider these results to be a strong demonstration of the value of using UAV and satellite imagery in tandem to investigate forest-wide effects at an individual tree level.
This paper compares two approaches developed by the National Research Council of Canada to conduct “near-miss” intercepts in flight test, and describes a new method for assessing the efficacy of these trajectories. Each approach used a different combination of flight test techniques and displays to provide guidance to the pilots to set-up the aircraft on a collision trajectory and to maintain the desired path. Approach 1 only provided visual guidance of the relative azimuth and position of the aircraft, whereas Approach 2 established the conflict point (latitude/longitude) from the desired geometry, and provided cross track error from the desired intercept as well as speed cueing for the arrival time. The performance of the approaches was analyzed by comparing the proportion of time where the predicted closest approach distance was below a desired threshold value. The analysis showed that Approach 2 resulted in more than double the amount of time spent at or below desired closest approach distance across all azimuths flown. Moreover, since less time was required to establish the required initial conditions, and to stabilize the flight paths, the authors were able to conduct 50% more intercepts.
