Drones, Volume 6, Issue 3 (March 2022) – 25 articles

Unmanned Aerial Vehicles (UAVs) for 3D indoor mapping applications are often equipped with bulky and expensive sensors, such as LIDAR (Light Detection and Ranging) or depth cameras. The same task could be also performed by inexpensive RGB cameras installed on light and small platforms that are more agile to move in confined spaces, such as during emergencies. However, this task is still challenging because of the absence of a GNSS (Global Navigation Satellite System) signal that limits the localization (and scaling) of the UAV. The reduced density of points in feature-based monocular SLAM (Simultaneous Localization and Mapping) then limits the completeness of the delivered maps. In this paper, the real-time capabilities of a commercial, inexpensive UAV (DJI Tello) for indoor mapping are investigated. The work aims to assess its suitability for quick mapping in emergency conditions to support First Responders (FR) during rescue operations in collapsed buildings. The proposed solution only uses images in input and integrates SLAM and CNN-based (Convolutional Neural Networks) Single Image Depth Estimation (SIDE) algorithms to densify and scale the data and to deliver a map of the environment suitable for real-time exploration. The implemented algorithms, the training strategy of the network, and the first tests on the main elements of the proposed methodology are reported in detail. The results achieved in real indoor environments are also presented, demonstrating performances that are compatible with FRs’ requirements to explore indoor volumes before entering the building. Full article

Distributed electric propulsion (DEP) aircrafts have high propulsion efficiency and low fuel consumption, which is very promising for propulsion. The redundant thrusters of DEP aircrafts increase the risk of fault in the propulsion system, so it is necessary to study fault-tolerant control to ensure flight safety. There has been little research on coordinated thrust control, and research on fault-tolerant control of the propulsion system for DEP aircrafts is also in the preliminary stage. In this study, a mathematical model of DEP aircrafts was built. Aiming at the lateral and longitudinal control of DEP aircrafts, a coordinated thrust control method based on total energy control and total heading control was designed. Furthermore, a fault-tolerant control strategy and control method was developed for faults in the propulsion system. Simulation results showed that the controller could control the thrust to the prefault level. The correctness and effectiveness of the designed coordinated thrust control method and the fault-tolerant control method for DEP aircrafts were theoretically verified. This study provides a theoretical basis for future engineering application and development of the control system for DEP aircrafts. Full article

As combat missions become increasingly complex in both space and time, cross-regional joint operations (CRJO) is becoming an overwhelming trend in modern air warfare. How to allocate resources and missions prior to the operation becomes a central issue to improve the combat efficiency. In this paper, we focus on the cooperative mission planning of multiple heterogeneous unmanned aerial vehicles (UAVs) in a CRJO. A multi-objective optimization problem is presented with the aim of minimizing the makespan while maximizing the value expectation obtained. Moreover, it is not mandatory for each UAV to return exactly to the base which it takes off. Furthermore, in addition to the constraints commonly found in UAV mission assignment problems, the ammunition inventory at each base is also taken into account. To solve such a problem, we developed an improved genetic algorithm (IGA) with a novel chromosome encoding format. It can determine the number of attacks on a given target based on the expectations obtained, rather than being predetermined. Specifically, an efficient logic-based unlocking mechanism is designed for the crossover and mutation operations in the algorithm. Simulation results show that the developed IGA can efficiently solve the considered problem. Through numerical experimental comparisons, the algorithm proposed in this work is superior to other existing IGA-like algorithms in terms of computational efficiency. Full article

Due to the large amount of video data from UAV aerial photography and the small target size from the aerial perspective, pedestrian detection in drone videos remains a challenge. To detect objects in UAV images quickly and accurately, a small-sized pedestrian detection algorithm based on the weighted fusion of static and dynamic bounding boxes is proposed. First, a weighted filtration algorithm for redundant frames was applied using the inter-frame pixel difference algorithm cascading vision and structural similarity, which solved the redundancy of the UAV video data, thereby reducing the delay. Second, the pre-training and detector learning datasets were scale matched to address the feature representation loss caused by the scale mismatch between datasets. Finally, the static bounding extracted by YOLOv4 and the motion bounding boxes extracted by LiteFlowNet were subject to the weighted fusion algorithm to enhance the semantic information and solve the problem of missing and multiple detections in UAV object detection. The experimental results showed that the small object recognition method proposed in this paper enabled reaching an mAP of 70.91% and an IoU of 57.53%, which were 3.51% and 2.05% higher than the mainstream target detection algorithm. Full article

Drones have become popular with the general public for viewing and filming marine life. One amateur enthusiast platform, DroneSharkApp, films marine life in the waters off Sydney, Australia year-round and posts their observations on social media. The drone observations include the behaviours of a variety of coastal marine wildlife species, including sharks, rays, fur seals, dolphins and fish, as well as migratory species such as migrating humpback whales. Given the extensive effort and multiple recordings of the presence, behaviour and interactions of various species with humans provided by DroneSharkApp, we explored its utility for providing biologically meaningful observations of marine wildlife. Using social media posts from the DroneSharkApp Instagram page, a total of 678 wildlife videos were assessed from 432 days of observation collected by a single observer. This included 94 feeding behaviours or events for fur seals (n = 58) and dolphins (n = 33), two feeding events for white sharks and one feeding event for a humpback whale. DroneSharkApp documented 101 interactions with sharks and humans (swimmers and surfers), demonstrating the frequent, mainly innocuous human–shark overlap off some of Australia’s busiest beaches. Finally, DroneSharkApp provided multiple observations of humpback and dwarf minke whales with calves travelling north, indicating calving occurring well south of traditional northern Queensland breeding waters. Collaboration between scientists and citizen scientists such as those involved with DroneSharkApp can greatly and quantitatively increase the biological understanding of marine wildlife data. Full article

The field of Unmanned Aerial Vehicles (UAVs) has progressed greatly in the last years. UAVs are now used for many applications and are often flown automatically. One commonly implemented feature in an automatic flight is that of following a mission at a stable altitude. However, this altitude is almost always referenced from the take-off location and does not take terrain profile levels into account. This is a critical and dangerous issue because if the terrain level changes abruptly (e.g., mountain regions or buildings in a city), this can lead to crashes or an unintended (illegal) high altitude. Our aim for this work is to provide a solution such that a constant altitude above ground level is maintained. To this end, we make use of the readily available Digital Elevation Models (DEMs). These models, which contain the terrain elevation, help us in dynamically adjusting the VTOL UAV altitude so that it remains nearly constant in relation to the ground. Results have shown that with the use of our method, the altitude can be maintained sufficiently constant while introducing a limited increase in flight time and battery consumption that is proportional to the terrain’s irregularity. In a moderately changing terrain, the error could be reduced to just $\pm 5$ m. Full article

Accurate retrieval of grassland traits is important to support management of pasture production and phenotyping studies. In general, conventional methods used to measure forage yield and quality rely on costly destructive sampling and laboratory analysis, which is often not viable in practical applications. Optical imaging systems carried as payload in Unmanned Aerial Vehicles (UAVs) platforms have increasingly been proposed as alternative non-destructive solutions for crop characterization and monitoring. The vegetation spectral response in the visible and near-infrared wavelengths provides information on many aspects of its composition and structure. Combining spectral measurements and multivariate modelling approaches it is possible to represent the often complex relationship between canopy reflectance and specific plant traits. However, empirical models are limited and strictly represent characteristics of the observations used during model training, therefore having low generalization potential. A method to mitigate this issue consists of adding informative samples from the target domain (i.e., new observations) to the training dataset. This approach searches for a compromise between representing the variability in new data and selecting only a minimal number of additional samples for calibration transfer. In this study, a method to actively choose new training samples based on their spectral diversity and prediction uncertainty was implemented and tested using a multi-annual dataset. Accurate predictions were obtained using hyperspectral imagery and linear multivariate models (Partial Least Squares Regression—PLSR) for grassland dry matter (DM; $R^2$ = 0.92, $RMSE$ = 3.25 dt ha${}^{-1}$), nitrogen (N) content in % of DM ($R^2$ = 0.58, $RMSE$ = 0.27%) and N-uptake ($R^2$ = 0.91, $RMSE$ = 6.50 kg ha${}^{-1}$). In addition, the number of samples from the target dates added to the training dataset could be reduced by up to 77% and 74% for DM and N-related traits, respectively, after model transfer. Despite this reduction, $RMSE$ values for optimal transfer sets (identified after validation and used as benchmark) were only 20–30% lower than those values obtained after model transfer based on prediction uncertainty reduction, indicating that loss of accuracy was relatively small. These results demonstrate that considerably simple approaches based on UAV hyperspectral data can be applied in preliminary grassland monitoring frameworks, even with limited datasets. Full article

Unmanned aerial systems (UASs) have enormous potential in many fields of application, especially when used in combination with autonomous guidance. An open challenge for safe autonomous flight is to rely on a mapping system for local positioning and obstacle avoidance. In this article, the authors propose a radar-based mapping system both for obstacle detection and for path planning. The radar equipment used is a single-chip device originally developed for automotive applications that has good resolution in azimuth, but poor resolution in elevation. This limitation can be critical for UAS application, and it must be considered for obstacle-avoidance maneuvers and for autonomous path-planning selection. However, the radar-mapping system proposed in this paper was successfully tested in the following different scenarios: a single metallic target in grass, a vegetated scenario, and in the close proximity of a ruined building. Full article

The development of UAV sensors has made it possible to obtain a diverse array of spectral images in a single flight. In this study, high-resolution UAV-derived images of urban areas were employed to create land cover maps, including car-road, sidewalk, and street vegetation. A total of nine orthoimages were produced, and the variables effective in producing UAV-based land cover maps were identified. Based on analyses of the object-based images, 126 variables were derived by computing 14 statistical values for each image. The random forest (RF) classifier was used to evaluate the priority of the 126 variables. This was followed by optimizing the RF through variable reduction and by comparing the initial and optimized RF, the utility of the high-priority variable was evaluated. Computing variable importance, the most influential variables were evaluated in the order of normalized digital surface model (nDSM), normalized difference vegetation index (NDVI), land surface temperature (LST), soil adjusted vegetation index (SAVI), blue, green, red, rededge. Finally, no significant changes between initial and optimized RF in the classification were observed from a series of analyses even though the reduced variables number was applied for the classification. Full article

The internal transport of medical goods in a hospital heavily relies on human resources that carry the materials on foot. Such mode of transport may be affected by inefficiencies, e.g., due to bottlenecks, and other logistic challenges. Thus, it may benefit from the use of unmanned aircraft systems in several aspects. Such a scenario introduces specific criticalities for healthcare organizations in densely populated areas and below congested airspace, such as the Milan metropolitan area. The authors applied a co-creation methodology to design a highly automated drone service for the delivery of pharmaceuticals at San Raffaele Hospital, Milan, Italy. The needs of the main users were identified by means of semi-structured interviews and visualization material. Based on those outcomes, a drone service was designed and validated with the main users. It emerged that the main gain point of such a service would be increasing hospital logistics efficiency. The risks tied to the operations (e.g., tampering of the delivery container) were evaluated and appropriate mitigations were identified (e.g., use of tamper-evident seals or mechatronic locks). The information required by the digital system offering the needed logistics functions was analyzed for future development. Recent conceptual and regulatory advancements in the field of Urban Air Mobility (UAM) in Europe were elaborated to outline the digital ecosystem in which aviation and non-aviation actors would exchange information to ensure operations’ efficiency, safety and regulatory compliance. Full article

This paper addresses an optimization of Unmanned Aerial Vehicle (UAV) flight trajectories by bank-turn mechanism for a fixed-wing UAV at a constant altitude. The flight trajectories should be optimal and stay in the UAV flight operational area. The maneuver planning is conducted in two steps, which are UAV path planning and UAV flight trajectory planning. For the first step, the Bezier curve is employed as a maneuvering path. The path planning optimization objective is to minimize the path length while satisfying maximum curvature and collision avoidance constraints. The flight trajectories optimization objective is to minimize maneuvering time and load factor considering, minimum/maximum speed, minimum/maximum acceleration, maximum roll angle, maximum turn rate, and aerodynamics constraints. The variable speed trajectory generation is developed within allowable speed zone considering these UAV flight constraints by employing meta-heuristic optimizations. Results show that the PSO have outperformed the GA and the GWO for both steps of path planning and trajectory planning. The variable speed has succeeded in reducing the load factor during the bank-turn mechanism using the Bezier curve. The variable speed is recommended to be conducted when the result of the maneuvering path involve the lower turning radius. A simultaneous on arrival target mission has also succeeded to be conducted using the combination of the variable speed and constant speed strategies. Full article

The icing facilities of the Anti-Icing Materials International Laboratory AMIL have been adapted to reproduce icing conditions on a Bell APT70 drone rotor, typical of small-to-medium UAV models. As part of an extensive icing test campaign, this paper presents the design and preliminary testing of the experimental setup and representative icing conditions calibration in the laboratory’s cold chamber. The drone rotor used has four blades with a diameter of 0.66 m and a maximum tip speed of 208 m/s. For the icing conditions, freezing rain and freezing drizzle were selected. A Liquid Water Content (LWC) calculation methodology for a rotor in hover was developed, and procedures to determine experimental LWC in the facility are presented in this paper. For the test setup, the cold chamber test section was adapted to fit the rotor and to control its ground clearance. Testing was aimed at studying the effect of rotor height h on aerodynamic performance, both with and without icing conditions. Results show no significant effect on the ground effect between h = 2 m and h = 4 m in dry runs, while the icing behavior can be largely influenced for certain conditions by the proximity of the precipitation source, which depend on the height of the rotor in these experiments. Full article

Multiple unmanned multirotor (MUM) systems are becoming a reality. They have a wide range of applications such as for surveillance, search and rescue, monitoring operations in hazardous environments and providing communication coverage services. Currently, an important issue in MUM is coverage control. In this paper, an existing coverage control algorithm has been extended to incorporate a new sensor model, which is downward facing and allows pan-tilt-zoom (PTZ). Two new constraints, namely view angle and collision avoidance, have also been included. Mobile network coverage among the MUMs is studied. Finally, the proposed scheme is tested in computer simulations. Full article

In a departure from the past, unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs) are increasingly needed for complementary cooperation in military, scientific, and commercial applications, because this is more efficient than standalone operations. Information sharing through acoustic underwater communication is vital for complementary cooperation between USVs and UUVs. Normally, since USVs have advantages in terms of wide operational boundaries compared to UUVs, they are efficient for tracking UUVs. In this paper, we suggest a UUV tracking algorithm for a USV. The tracking algorithm’s development consists of three main software models: an estimation based on an extended Kalman filter (EKF) with a navigation smoothing method, guidance based on multimode guidance, and re-searching based on a pattern. In addition, the algorithm provides a procedure for tracking UUVs in complex acoustic underwater communication environments. The tracking algorithm was tested in a simulated environment to check the performance of each method, and implemented with a USV system to verify its validity and stability in sea trials. The UUV tracking algorithm of the USV shows stable and efficient performance. Full article

In recent years, the drone market has had a significant expansion, with applications in various fields (surveillance, rescue operations, intelligent logistics, environmental monitoring, precision agriculture, inspection and measuring in the construction industry). Given their increasing use, the issues related to safety, security and privacy must be taken into consideration. Accordingly, the development of new concepts for countermeasures systems, able to identify and neutralize a single (or multiples) malicious drone(s) (i.e., classified as a threat), has become of primary importance. For this purpose, the paper evaluates the concept of a multiplatform counter-UAS system (CUS), based mainly on a team of mini drones acting as a cooperative defensive system. In order to provide the basis for implementing such a system, we present a review of the available technologies for sensing, mitigation and command and control systems that generally comprise a CUS, focusing on their applicability and suitability in the case of mini drones. Full article

Uncrewed Aerial Vehicles (UAV) constitute a rapidly evolving technology field that is becoming more accessible and capable of supplementing, expanding, and even replacing some traditionally manual bridge inspections. Given the classification of the bridge inspection types as initial, routine, in-depth, damage, special, and fracture critical members, specific UAV mission requirements can be developed, and their suitability for UAV application examined. Results of a review of 23 applications of UAVs in bridge inspections indicate that mission sensor and payload needs dictate the UAV configuration and size, resulting in quadcopter configurations being most suitable for visual camera inspections (43% of visual inspections use quadcopters), and hexa- and octocopter configurations being more suitable for higher payload hyperspectral, multispectral, and Light Detection and Ranging (LiDAR) inspections (13%). In addition, the number of motors and size of the aircraft are the primary drivers in the cost of the vehicle. 75% of vehicles rely on GPS for navigation, and none of them are capable of contact inspections. Factors that limit the use of UAVs in bridge inspections include the UAV endurance, the capability of navigation in GPS deprived environments, the stability in confined spaces in close proximity to structural elements, and the cost. Current research trends in UAV technologies address some of these limitations, such as obstacle detection and avoidance methods, autonomous flight path planning and optimization, and UAV hardware optimization for specific mission requirements. Full article

This study explores the application of small, commercially available drones to determine morphometric the measurements and record key demographic parameters of reef manta rays (Mobula alfredi) in Raja Ampat, Indonesia. DJI Mavic 2 Pro drones were used to obtain videos of surface-feeding M. alfredi with a floating, known-length PVC pipe as a reference scale—thus avoiding the need to utilize altitude readings, which are known to be unreliable in small drones, in our photogrammetry approach. Three dimensions (disc length (DL), disc width (DW), and cranial width (CW)) from 86 different individuals were measured. A hierarchical multivariate model was used to estimate the true measurements of these three dimensions and their population-level multivariate distributions. The estimated true measurements of these dimensions were highly accurate and precise, with the measurement of CW more accurate than that of DL and, especially, of DW. Each pairing of these dimensions exhibited strong linear relationships, with estimated correlation coefficients ranging from 0.98–0.99. Given these, our model allows us to accurately calculate DW (as the standard measure of body size for mobulid rays) using the more accurate CW and DL measurements. We estimate that the smallest mature M. alfredi of each sex we measured were 274.8 cm (males, n = 30) and 323.5 cm DW (females, n = 8). We conclude that small drones are useful for providing an accurate “snapshot” of the size distribution of surface-feeding M. alfredi aggregations and for determining the sex and maturity of larger individuals, all with minimal impact on this vulnerable species. Full article

Unmanned aerial vehicles (UAVs, drones) are expected to save transport time and improve service reliability for transport of biologic samples, but few studies have evaluated the potential time savings of such services. The total transport time defined as time from sample ready for transport until arrival at the laboratory was used to assess the absolute and relative time savings of drones compared with ground transport, using ground distances from 4–7 km (urban model) to 179–262 km (rural district routes) with one to eight daily scheduled trips. Costs of existing ground transport were allocated to drone flight times as a proxy for drone cost competitiveness. Time savings were less than 20–30% in the urban model but 65–74% in the rural routes using drone speeds of 100 km/h, but the time between trips (route frequencies) and drone speeds influenced the relative time savings substantially. Cost of time gains per number of samples was less favorable using drones in the rural models due to lower transport volumes. This research concludes that drone solutions provide marginal gains for short-distance transports, whereas time savings are more promising in long transport models with appropriate scheduling and sufficiently high drone speeds. Full article

The capabilities of a quadcopter in the hover mode for low-altitude sensing of atmospheric turbulence with high spatial resolution in urban areas characterized by complex orography are investigated. The studies were carried out in different seasons (winter, spring, summer, and fall), and the quadcopter hovered in the immediate vicinity of ultrasonic weather stations. The DJI Phantom 4 Pro quadcopter and AMK-03 ultrasonic weather stations installed in different places of the studied territory were used in the experiment. The smoothing procedure was used to study the behavior of the longitudinal and lateral spectra of turbulence in the inertial and energy production ranges. The longitudinal and lateral turbulence scales were estimated by the least-square fit method with the von Karman model as a regression curve. It is shown that the turbulence spectra obtained with DJI Phantom 4 Pro and AMK-03 generally coincide, with minor differences observed in the high-frequency region of the spectrum. In the inertial range, the behavior of the turbulence spectra shows that they obey the Kolmogorov–Obukhov “5/3” law. In the energy production range, the longitudinal and lateral turbulence scales and their ratio measured by DJI Phantom 4 Pro and AMK-03 agree to a good accuracy. Discrepancies in the data obtained with the quadcopter and the ultrasonic weather stations at the territory with complex orography are explained by the partial correlation of the wind velocity series at different measurement points and the influence of the inhomogeneous surface. Full article

Much research has been accomplished in the area of drone landing and specifically pose estimation. While some of these works focus on sensor fusion using GPS, or GNSS, we propose a method that uses sensors, including four Time of Flight (ToF) range sensors and a monocular camera. However, when the descending platform is unstable, for example, on ships in the ocean, the uncertainty will grow, and the tracking will fail easily. We designed an algorithm that includes four ToF sensors for calibration and one for pose estimation. The landing process was divided into two main parts, the rendezvous and the final landing. Two important assumptions were made for these two phases. During the rendezvous, the landing platform movement can be ignored, while during the landing phase, the drone is assumed to be stable and waiting for the best time to land. The current research modifies the landing part as a stable drone and an unstable landing platform, which is a Stewart platform, with a mounted AprilTag. A novel algorithm for calibration was used based on color thresholding, a convex hull, and centroid extraction. Next, using the homogeneous coordinate equations of the sensors’ touching points, the focal length in the X and Y directions can be calculated. In addition, knowing the plane equation allows the Z coordinates of the landmark points to be projected. The homogeneous coordinate equation was then used to obtain the landmark’s X and Y Cartesian coordinates. Finally, 3D rigid body transformation is engaged to project the landing platform transformation in the camera frame. The test bench used Software-in-the-Loop (SIL) to confirm the practicality of the method. The results of this work are promising for unstable landing platform pose estimation and offer a significant improvement over the single-camera pose estimation AprilTag detection algorithms (ATDA). Full article

The use of unmanned aerial vehicles (UAVs, also known as drones or RPA) in archaeology has expanded significantly over the last twenty years. Improvements in terms of the reliability, size, and manageability of these aircraft have been largely complemented by the high resolution and spectral bands provided by the sensors of the different cameras that can be incorporated into their structure. If we add to this the functionalities and improvements that photogrammetry programs have been experiencing in recent years, we can conclude that there has been a qualitative leap in the possibilities, not only of geometric documentation and in the presentation of the archaeological data, but in the incorporation of non-intrusive high-resolution analytics. The work that we present here gives a sample of the possibilities of both geometric documentation, creation of 3D models, their subsequent printing with different materials, and techniques to finally show a series of analytics from images with NGB (Nir + Green + Blue), Red Edge, and Thermographic cameras applied to various archaeological sites in which our team has been working since 2013, such as Clunia (Peñalba de Castro, Burgos), Puig Rom (Roses), Vilanera (L’Escala, Girona), and Cosa (Ansedonia, Italy). All of them correspond to different chronological periods as well as to varied geographical and morphological environments, which will lead us to propose the search for adequate solutions for each of the environments. In the discussions, we will propose the lines of research to be followed in a project of these characteristics, as well as some results that can already be viewed. Full article

A biplane quadrotor is a hybrid type of Unmanned Aerial Vehicle (UAV) that has advantages of both fixed-wing and rotary-wing UAVs. In this study, we design controllers using (i) Backstepping Control (BSC), (ii) Integral Terminal Sliding Mode Control (ITSMC), and (iii) Hybrid control (ITSMC + BSC), where the ITSMC controls attitude and BSC controls the altitude subsystems as per the mathematical model of biplane quadrotor. The performance of these controllers is evaluated based on the autonomous trajectory tracking containing all possible maneuvers and operation modes that the biplane quadrotor can perform. Performance analysis reveals that the BSC-based controller is susceptible to a steady-state error in altitude tracking when mass is changed. In contrast, the ITSMC and the “hybrid” controllers achieve smooth tracking in a finite time. Furthermore, the “hybrid” controller outperforms the other designs, reducing tracking error and faster convergence time. Full article

Unmanned aerial system (UAS) traffic management (UTM) requires each UAS to communicate with each other and to other stakeholders involved in the operation. In practice, there are two types of wireless communication systems established in the UAS community: automatic dependent surveillance broadcast (ADS-B) and remote identification (Remote ID). In between these two systems, there is ADS-B-like communication which leverages using other types of communications available in the market for the purpose of UTM. This review aims to provide an insight into those three systems, based on the published standard documents and latest research development. It also suggests how to construct a feasible communication architecture. The integrative approach is used in this literature review. The review categorization includes definition, data format, technology used, and research applications, and any remaining issues are discussed. The similarities and differences of each system are elaborated, covering practical findings. In addition, the SWOT analysis is conducted based on the findings. Lastly, multi-channel communication for UTM is proposed as a feasible solution in the UTM operation. Full article

“Flight Time” and the “Scope of the mission” play major roles in using UAVs as they affect most industrial activities. Once the battery has depleted, the UAV has to land on the ground and human interaction is needed to change the battery with a fully charged one. Nowadays, several automatic battery swapping systems are catching interest in research. This research presents the novel concept of an Inverted Docking Station that allows a quadrotor UAV to attach to the ceiling during the automatic battery-swapping process. The proposed design consist of a docking station, a positioning system and gripper mechanisms. The proposed design allows the quadrotor to carry the load under the quadrotor and remain attached throughout the servicing period. A mathematical model and design guideline have been proposed, and a Finite Element Analysis (FEA) was performed to check that the developed platform is strong enough to withstand the above task. A ‘DJI TELLO’ small-scale quadrotor was chosen as a case study to demonstrate the proposed research. Finally the advantages and the limitations of the system are discussed. Full article

A small drone is capable of capturing distant objects at a low cost. In this paper, long distance (up to 1 km) ground target tracking with a small drone is addressed for oblique aerial images, and two novel approaches are developed. First, the coordinates of the image are converted to real-world based on the angular field of view, tilt angle, and altitude of the camera. Through the image-to-position conversion, the threshold of the actual object size and the center position of the detected object in real-world coordinates are obtained. Second, the track-to-track association is improved by adopting the nearest neighbor association rule to select the fittest track among multiple tracks in a dense track environment. Moving object detection consists of frame-to-frame subtraction and thresholding, morphological operation, and false alarm removal based on object size and shape properties. Tracks are initialized by differencing between the two nearest points in consecutive frames. The measurement statistically nearest to the state prediction updates the target’s state. With the improved track-to-track association, the fittest track is selected in the track validation region, and the direction of the displacement vector and velocity vectors of the two tracks are tested with an angular threshold. In the experiment, a drone hovered at an altitude of 400 m capturing video for about 10 s. The camera was tilted 30° downward from the horizontal. Total track life (TTL) and mean track life (MTL) were obtained for 86 targets within approximately 1 km of the drone. The interacting multiple mode (IMM)-CV and IMM-CA schemes were adopted with varying angular thresholds. The average TTL and MTL were obtained as 84.9–91.0% and 65.6–78.2%, respectively. The number of missing targets was 3–5; the average TTL and MTL were 89.2–94.3% and 69.7–81.0% excluding the missing targets. Full article