Summary This journal (Drone Systems and Applications; DSA) conducted a targeted “horizon scan” during 2022 within our team of editors and associate editors. We asked— Which research areas currently under-represented in Drone Systems and Applications would you like to see more heavily represented in the future? The process highlighted five areas of interest and potential growth: Drones in the geosciences Aquatic drones Ground drones Drones within calibration/validation experiments Drones and computer vision Over the past two years (2020–22), the journal has published over 50 papers with a strong leaning towards aerial drones for ecology and also with an engineering focus. DSA is keen to receive new submissions addressing the five highlighted areas, which lie firmly within the aims and scope of the journal. Further to the horizon scan, we propose two special collections for the coming year—one addressing drone applications ( drones in geoscience applications) and a second addressing drone systems ( aquatic drone systems). We would like to hear from scientists and practitioners in these fields as both contributors and (or) collection editors.

Drones are expected to become widespread in society, making public readiness an important prerequisite for successfully unleashing them. This article investigates Danish citizens’ opinions on drones across varying fields of application and, specifically, six potential cases of healthcare logistics. Survey data representative of age, gender, and geography were collected and included information about respondents’ background, knowledge level of drones, and opinions on different drone use cases. Data were analysed with frequency tables and bivariate cross-tabulation. A thousand and four Danish adults completed the survey. Although other fields of application received higher levels of support, a majority of the respondents were positive towards using drones for healthcare logistics. Transportation of medicine and blood samples between hospitals were the most accepted healthcare use cases. Support varies across age with the highest support found in the eldest age group. Also, the more citizens report to know about drones, the more they tend to support using them. The results suggest that policymakers and firms must be attentive towards the public opinion on drones and seek insights into what citizens regard as noble purposes of using drones. Moreover, citizens must become more acquainted with drones, as this will likely boost public support.

Employment of unmanned aerial vehicles (UAVs) or drones as swarms of coordinating nodes offers multiple advantages for commercial as well as military applications. However, the complex communication requirements of these swarms, coupled with high data rates of advanced UAV payloads, require innovative techniques for optimizing data throughput. Channel capacity being the key resource, optimum communication architecture and network topology is critical to ensure quality of service while remaining within transmission power constraints. This paper proposes a capacity maximization approach for swarm communication architectures using mixed-integer nonlinear programming (MINLP). These techniques are designed to tackle optimization applications involving both discrete variables and nonlinear system dynamics. Mathematical model formulated considering system constraints and desired objective function establishes applicability of MINLP. Since MINLP problems are NP-hard in general, computational overheads and search space exponentially grow with number of nodes in the swarm. Therefore, outer approximation algorithm has been applied that achieves near-optimal solutions with reduced convergence time and complexity compared with exhaustive search. Applicability of algorithm regardless of selected communication architecture has been established through realistic simulations.

For the first time ever, samples were collected from volcanic lake waters in Costa Rica using an unmanned aerial vehicle (drone), which represents a major achievement in human–machine interaction and innovation in the technology sector. A Matrice 600 Pro drone was used for remote sampling in the hyperacid crater lake of the Poás volcano, the mildly acidic Lake Botos, and the nearly neutral Lake Hule. A bailer bottle of 250 mL and a HOBO temperature probe, mounted on the drone, were deployed using a specially designed delivery retrieval system. A comparison was carried out relating to the geochemistry of lake water collected by drone as opposed to the hand-collected samples. The SO4−2/Cl ratios of the two samples at Poás hyperacid crater lake were similar, (1.1 ± 0.2) on average, an indication of a lake with homogenous water composition. The Lake Hule showed a similar composition to that registered 20 years ago. The waters from Lake Botos showed some differences, which may be explained by the influence of springs at the bottom of the lake, but the Wilcoxon's signed-rank test showed a good exhibit of a satisfactory level of similarity. Autonomous navigation proves to be very useful for faster, more efficient, reliable, and less hazardous sampling of volcanic lakes.

This article proposes a mobile robot localization system developed using Google Indoor Street View (GISV) and Convolutional Neural Network (CNN)-based visual place recognition. The proposed localization system consists of two main modules. The first is a place recognition module based on GISV and a net Vector of Locally Aggregated Descriptors (VLAD)-based CNN. The second is a factor graph-based optimization module. In this work, we show that a CNN-based approach can be utilized to overcome the lack of visually distinct features in indoor environments and changes in images that can occur when using different cameras at different points in time for localization. The proposed CNN-based localization system is implemented using reference and query images obtained from two different sources (GISV and a camera attached to a mobile robot). It has been experimentally validated using a custom indoor dataset captured at the Memorial University of Newfoundland engineering building basement. The main results of this paper show that GISV-based place recognition reduces the percentage drift by 4% for the dataset and achieves a Root Mean Square Error (RMSE) of 2 m for position and 2.5° for orientation.

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.