Airborne Drones Research Articles

Investigations into optimum non‐periodic bursts scheme in radar pulse train designed for fixed‐wing drone detections by multi‐function radar

AbstractDrone surveillance with multi‐function radar (MFR) can benefit a lot from the careful radar pulse train organisation into dedicated sub‐RPTs serving particular needs of tasks. In contemporary scenarios of MFR applications, where navigation, search and rescue, and natural environment safeguarding are to be shared, of particular importance is a case where airborne small drone detection radar performs other tasks concurrently. Resource sharing within the radar unit is based on variables and unpredictable circumstances, leading to strains in resources in MFR. Consequently, such constraints limit MFR functioning with optimal drone sensitivity for its own sake. To ensure a high quality of radar services performed quickly, with a low DC power consumption, the task sharing approach by the common MFR platform, non‐periodic bursts emission of radar pulse energy has gained our major attention. At the same time, it was assumed that bursts feature periodic time intervals between radar pulses, which is a favourable circumstance in the control and implementation of signal processing. Each sub‐RPTs is designed to cope with particular detection tasks. One to four linear polarisations (VV, VH, HV, HH) are activated. The focus of the investigation was on the radar emitting a few thousand pulses per second, but only a few 10 pulses per second were expected to be required to detect small airborne drones. The core of the research study was the optimisation process of the scheme operating with 4 to 16 pulse bursts optimised for drone detection with a modest use of resources at MFR. While performing several activities, the X‐band MFR platform called ENAVI was developed in‐house. The proposed non‐periodic burst scheme optimisation approach was validated with in‐field tests with the ENAVI radar model. The in‐air target was a 3‐metre fixed‐wing drone having low RCS and flying at different altitudes with a speed close to 200 km/h. The radar antenna was a 26 dBi low‐profile dual‐polarisation array making it feasible to detect drones remaining a few degrees off the antenna breadboard direction. A very high likelihood of detection of drones from a few kilometres distance was demonstrated within one second. The authors present the effectiveness of the cell‐averaging technique which proved to correctly detect drones within a 7‐km range, and how MFR radar echoes can be used for basic weather surveillance. One vertical VV polarisation was used for drone detection and two VV and VH polarisations for weather observation. Weather radar capabilities were examined against heavy rain clouds imposing a hazard to the safety of drone flights.

A reinforcement learning (RL)-based hybrid method for ground penetrating radar (GPR)-driven buried object detection

Ground penetrating radar (GPR) systems are effective sensors for discovering various types of objects buried underground, such as military mines, metal objects, and pieces of underground infrastructures. A GPR system can be manually operated by a human or can be an integral part of a host platform. The host platform may be semi- or fully autonomous and may operate in different environments such as land vehicles or more recently air-borne drones. One challenge for the fully or semi-autonomous host platforms in particular is to find an efficient search procedure that would reduce the operation time and optimize resource utilization. Most of the current approaches are based on pre-defined search patterns which, for large and sparse areas, could mean unnecessary waste of time and resources. In this paper, we introduce a method that combines a coarse and therefore relatively low cost initial search pattern with a Reinforcement Learning (RL) driven efficient navigation path for eventual target detection, by exploiting the signal processing pipeline of the onboard GPR. We illustrate the applicability of the method using a well-known, high fidelity GPR simulation environment and a novel RL framework. Our results suggest that combination of a coarse navigation scheme and an RL-based training procedure based on GPR scan returns can lead to a more efficient target discovery procedure for host platforms.

Narco drones: tracing the evolution of cartel aerial tactics in Mexico’s low-intensity conflicts

ABSTRACT This study examines the evolution of drone tactics employed by drug cartels in Mexico from 2017 to 2022. The research traces the increasing sophistication of drone technology, payload capacities, and adaptability of cartels in employing airborne drones in low-intensity conflicts It also highlights the increasing reliance on drones for various purposes. The analysis reveals insights into how criminal organizations adapt to the changing technological landscape, incorporating drones into their operations and creating new challenges for law enforcement. The analysis outlines strategies and identifies specialized measures to counteract this asymmetric warfare.

Airborne Drones for Water Quality Mapping in Inland, Transitional and Coastal Waters—MapEO Water Data Processing and Validation

Using airborne drones to monitor water quality in inland, transitional or coastal surface waters is an emerging research field. Airborne drones can fly under clouds at preferred times, capturing data at cm resolution, filling a significant gap between existing in situ, airborne and satellite remote sensing capabilities. Suitable drones and lightweight cameras are readily available on the market, whereas deriving water quality products from the captured image is not straightforward; vignetting effects, georeferencing, the dynamic nature and high light absorption efficiency of water, sun glint and sky glint effects require careful data processing. This paper presents the data processing workflow behind MapEO water, an end-to-end cloud-based solution that deals with the complexities of observing water surfaces and retrieves water-leaving reflectance and water quality products like turbidity and chlorophyll-a (Chl-a) concentration. MapEO water supports common camera types and performs a geometric and radiometric correction and subsequent conversion to reflectance and water quality products. This study shows validation results of water-leaving reflectance, turbidity and Chl-a maps derived using DJI Phantom 4 pro and MicaSense cameras for several lakes across Europe. Coefficients of determination values of 0.71 and 0.93 are obtained for turbidity and Chl-a, respectively. We conclude that airborne drone data has major potential to be embedded in operational monitoring programmes and can form useful links between satellite and in situ observations.

Intelligent indoor metasurface robotics.

Intelligent indoor robotics is expected to rapidly gain importance in crucial areas of our modern society such as at-home health care and factories. Yet, existing mobile robots are limited in their ability to perceive and respond to dynamically evolving complex indoor environments because of their inherently limited sensing and computing resources that are, moreover, traded off against their cruise time and payload. To address these formidable challenges, here we propose intelligent indoor metasurface robotics (I2MR), where all sensing and computing are relegated to a centralized robotic brain endowed with microwave perception; and I2MR's limbs (motorized vehicles, airborne drones, etc.) merely execute the wirelessly received instructions from the brain. The key aspect of our concept is the centralized use of a computation-enabled programmable metasurface that can flexibly mold microwave propagation in the indoor wireless environment, including a sensing and localization modality based on configurational diversity and a communication modality to establish a preferential high-capacity wireless link between the I2MR's brain and limbs. The metasurface-enhanced microwave perception is capable of realizing low-latency and high-resolution three-dimensional imaging of humans, even around corners and behind thick concrete walls, which is the basis for action decisions of the I2MR's brain. I2MR is thus endowed with real-time and full-context awareness of its operating indoor environment. We implement, experimentally, a proof-of-principle demonstration at ∼2.4GHz, in which I2MR provides health-care assistance to a human inhabitant. The presented strategy opens a new avenue for the conception of smart and wirelessly networked indoor robotics.

Autonomous Detection of Spodoptera frugiperda by Feeding Symptoms Directly from UAV RGB Imagery

The use of digital technologies to detect, position, and quantify pests quickly and accurately is very important in precision agriculture. Imagery acquisition using air-borne drones in combination with the deep learning technique is a new and viable solution to replace human labor such as visual interpretation, which consumes a lot of time and effort. In this study, we developed a method for automatic detecting an important maize pest—Spodoptera frugiperda—by its gnawing holes on maize leaves based on convolution neural network. We validated the split-attention mechanism in the classical network structure ResNet50, which improves the accuracy and robustness, and verified the feasibility of two kinds of gnawing holes as the identification features of Spodoptera frugiperda invasion and the degree. In order to verify the robustness of this detection method against plant morphological changes, images at the jointing stage and heading stage were used for training and testing, respectively. The performance of the models trained with the jointing stage images has been achieved the validation accuracy of ResNeSt50, ResNet50, EfficientNet, and RegNet at 98.77%, 97.59%, 97.89%, and 98.07%, with a heading stage test accuracy of 89.39%, 81.88%, 86.21%, and 84.21%.

The future of autonomous underwater vehicle control

The future of autonomous underwater vehicle control

Large device to device communication network including airborne drones for emergency scenarios

AbstractIn the case of emergency scenarios communication infrastructure might be destroyed, impairing communication abilities in the disaster area. In such situation device‐to‐device (D2D) communication networking plays an essential role to overcome the communication loss. To provide a reliable communication network, D2D devices are combined with airborne drones. The D2D devices provide a relay service while being able to transmit their own data. The airborne drones offer additional relay functionality and provide considerably reduced latency compared with D2D communication networks without any airborne drones. In this article, we will illustrate a large dynamic communication network (LDCN) and demonstrate its feasibility for even more than 64 000 D2D communication devices in the case of realistic channel conditions including path loss and fading. It will be shown that the illustrated LDCN is truly infrastructure‐less, that is, no infrastructure or backbone or base station functionality need to be reinstalled. Therefore, the airborne drones do not take over any coordination services. The illustrated LDCN has a simple packet switching architecture and since all communication is localized it is robust and fully flexible. In addition, no specific radio interface or preferred multiple access scheme such as code division multiple access or time division multiple access are required.

Sea State from Single Optical Images: A Methodology to Derive Wind-Generated Ocean Waves from Cameras, Drones and Satellites

Sea state is a key variable in ocean and coastal dynamics. The sea state is either sparsely measured by wave buoys and satellites or modelled over large scales. Only a few attempts have been devoted to sea state measurements covering a large domain; in particular its estimation from optical images. With optical technologies becoming omnipresent, optical images offer incomparable spatial resolution from diverse sensors such as shore-based cameras, airborne drones (unmanned aerial vehicles/UAVs), or satellites. Here, we present a standalone methodology to derive the water surface elevation anomaly induced by wind-generated ocean waves from optical imagery. The methodology was tested on drone and satellite images and compared against ground truth. The results show a clear dependence on the relative azimuth view angle in relation to the wave crest. A simple correction is proposed to overcome this bias. Overall, the presented methodology offers a practical way of estimating ocean waves for a wide range of applications.

Optimized Landing of Drones in the Context of Congested Air Traffic and Limited Vertiports

Drone fleet operators must be able to land the whole fleet in short notice. In practical operations, landing spots are usually much fewer than airborne drones. When many drones gravitate toward the limited landing spots simultaneously, congestion management becomes a challenge. This paper characterizes the fleet landing problem using mixed integer programming techniques and proposes a series of computational enhancements to reduce the solution time from hours to seconds. The solution algorithms are implemented in a software prototype for traffic management, and are thoroughly validated via extensive numerical examples and field simulations. For a fleet of 18 drones navigating at the same altitude layer within a 4-square kilometer area, all routing and trajectory computations can be completed in less than 5 seconds, and the entire fleet is able to complete landing at three pre-planned landing spots within about 3 minutes. Therefore, the models and algorithms are suitable for practical use.

Managing the drone revolution: A systematic literature review into the current use of airborne drones and future strategic directions for their effective control

Commercial and private deployment of airborne drones is revolutionising many ecosystems. To identify critical issues and research gaps, our systematic literature review findings suggest that historic issues such as privacy, acceptance and security are increasingly replaced by operational considerations including interaction with and impacts on other airspace users. Recent incidents show that unrestricted drone use can inflict problems on other airspace users like airports and emergency services. Our review of current regulatory approaches shows a need for further policy and management response to both manage rapid and efficient drone usage growth, and facilitate innovation (e.g. intraurban package delivery), with one promising strategic response being low altitude airspace management (LAAM) systems for all drone use cases.

The hype in spectral imaging

Hyperspectral imaging is currently a very well-known and much used technology for measuring features in different fields, such as chemistry, geology, medicine, food and agriculture, either spaceborne (satellites), airborne (drones) or at close proximity (e.g. field scanning, industrial sorting lines or microscopy). Its background is two-fold, and it can be considered as a special case of spectroscopy (“imaging spectroscopy”) or a special case of imaging (“spectral imaging”). Current practice is to use adjectives such as multi and hyper added to “spectral imaging” in order to characterise the number of wavelength bands. In this paper we propose the community to use scientifically sound terminology, like “imaging spectroscopy” or “spectral imaging”, without using ambiguous adjectives. Further, we encourage the community to define and agree upon clear adjectives to describe the number of variables in the naming of our imaging technique.

Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017

Monitoring long-term landslide activity is of importance for risk assessment and land management. Daytime airborne drones or very high-resolution optical satellites are often used to create landslide maps. However, such imagery comes at a high cost, making long-term risk analysis cost-prohibitive. Despite the widespread use of open-access 30 m Landsat imagery, their utility for landslide detection is often limited due to low classification accuracy. One of the major challenges is to separate landslides from other anthropogenic disturbances. Here, we produce landslide maps retrospectively from 1998 to 2017 for landslide-prone and highly populated Taiwan (35,874 km2). To improve classification accuracy of landslides, we integrate nighttime light imagery from the Defense Meteorological Satellite Program (DMSP) and the Visible Infrared Imaging Radiometer Suite (VIIRS), with multi-seasonal daytime optical Landsat time-series, and digital elevation data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). We employed a non-parametric machine-learning classifier, random forest, to classify the satellite imagery. The classifier was trained with data from three years (2005, 2010, and 2015), and was validated with an independent reference sample from twelve years. Our results demonstrated that combining nighttime light data and multi-seasonal imagery significantly improved the classification (p < 0.001), compared to conventional methods based on single-season optical imagery. The results confirmed that the developed classification model enabled mapping of landslides across Taiwan over a long period with annual overall accuracy varying between 96% and 97%, user's and producer's accuracies between 73% and 86%. Spatiotemporal analysis of the landslide inventories from 1998 to 2017 revealed different temporal patterns of landslide activities, showing those areas where landslides were persistent and other areas where landslides tended to reoccur after vegetation regrowth. In sum, we provide a robust method to detect long-term landslide activities based on freely available satellite imagery, which can be applied elsewhere. Our mapping effort of landslide spatiotemporal patterns is expected to be of high importance in developing effective landslide remediation strategies.

Productivity and changing technology

An orbital-class rocket with a 3D-printed engine launches into space from the Mähia Peninsula. A self-driving car crosses the Auckland Harbour Bridge. A pizza company begins testing delivery using airborne drones. While these may sound like things of science fiction, they are in fact stories that have been in the New Zealand media over the last year. These stories provide a glimpse of how technology is changing. Changes are not just happening around the edges but could be as disruptive to models of production as earlier industrial revolutions.

DTTA - Distributed, Time-division Multiple Access based Task Allocation Framework for Swarm Robots

Swarm robotic systems, unlike traditional multi-robotic systems, deploy number of cost effective robots which can co-operate, aggregate to form patterns/formations and accomplish missions beyond the capabilities of individual robot. In the event of fire, mine collapse or disasters like earthquake, swarm of robots can enter the area, conduct rescue operations, collect images and convey locations of interest to the rescue team and enable them to plan their approach in advance. Task allocation among members of the swarm is a critical and challenging problem to be addressed. DTTA- a distributed, Time-division multiple access (TDMA) based task allocation framework is proposed for swarm of robots which can be utilised to solve any of the 8 different types of task allocation problem identified by Gerkey and Mataric´. DTTA is reactive and supports task migration via extended task assignments to complete the mission in case of failure of the assigned robot to complete the task. DTTA can be utilised for any kind of robot in land or for co-operative systems comprising of land robots and air-borne drones. Dependencies with other layers of the protocol stack were identified and a quantitative analysis of communication and computational complexity is provided. To our knowledge this is the first work to be reported on task allocation for clustered scalable networks suitable for handling all 8 types of multi-robot task allocation problem. Effectiveness and feasibility of deploying DTTA in real world scenarios is demonstrated by testing the framework for two diverse application scenarios.

Using Competition to Control Congestion in Autonomous Drone Systems

With the number and variety of commercial drones and UAVs (Unmanned Aerial Vehicles) set to escalate, there will be high future demands on popular regions of airspace and communication bandwidths. This raises safety concerns and hence heightens the need for a generic quantitative understanding of the real-time dynamics of multi-drone populations. Here, we explain how a simple system design built around system-level competition, as opposed to cooperation, can be used to control and ultimately reduce the fluctuations that ordinarily arise in such congestion situations, while simultaneously keeping the on-board processing requirements minimal. These benefits naturally arise from the collective competition to choose the less crowded option, using only previous outcomes and built-in algorithms. We provide explicit closed-form formulae that are applicable to any number of airborne drones N, and which show that the necessary on-board processing increases slower than N as N increases. This design therefore offers operational advantages over traditional cooperative schemes that require drone-to-drone communications that scale like N 2 , and also over optimization and control schemes that do not easily scale up to general N. In addition to populations of drones, the same mathematical analysis can be used to describe more complex individual drones that feature N adaptive sensor/actuator units.

Factors Influencing Li-Ion Energy and Power Capabilities and Implications for Active Material Research

Li-ion technology enabled a revolution in consumer electronics by offering the highest energy density among rechargeable battery chemistries resulting in dramatically improved runtimes in a variety of devices. Given its unique combination of energy, power capability and life, lithium-ion technology is poised to revolutionize several emerging applications such as automotive, electric bikes, robotics, and air-borne drones for consumer and business applications. Cells used for each of these applications, which have markedly discharge rates and duty cycles, and must be optimized for energy density and specific energy. For example, the typical consumer electronics application has discharge rates of C/10, battery electric vehicles (BEVs) C/3, plug-in hybrid electric vehicles (PHEVs) 4C, hybrid electric vehicles (HEVs) 10 C, and drones 5C to 10 C. Designing a Li-ion cell with optimum energy density for each of these duty cycles requires not only selection of suitable active materials, but also suitable electrolytes and separators, and electrode designs that can support the specific duty cycles. At CAMX Power, we have been designing Li-ion cells for a wide range of applications including vehicles, electronics applications, lead-acid replacement batteries, and drone applications. Our work has revealed surprising impact of inactive materials on the energy and power capability of Li-ion cells. An important finding has been the role of the separator in determining the rate/power capability, and even energy density of Li-ion cells. Figure 1 shows the discharge rate performance of a high-nickel cathode material as a function of discharge rate for three different electrode loadings. The rate capability appears independent of loading for rates less than 1 C. However, at rates higher than 2C, the rate capability appears to depend strongly on the loading. From these data, it is tempting to conclude that this result is a consequence of ion-diffusion limitations in the thicker electrode – essentially the thickness of the electrode at the loading of 27.1 mg/cm2 is almost twice the thickness of the electrode at a loading of 15.6 mg/cm2. However, a different conclusion emerges if these same data are plotted as a function of areal current density instead of C-rate (Figure 2). The discharge capacities appear independent of the electrode loading, but depend strongly on the areal current density. Essentially, the ionic limitations are not in the electrode, but elsewhere in the cell. These studies have shown that the primary ionic limitation under these conditions occurs in the separator. Data presented in Figure 3 demonstrate this effect. Essentially, by changing only the separator the discharge capacity at a current density of 16 mA/cm2 (corresponding to 5 C) can be increased by a factor of four. We have used this improved understanding of the factors that control the rate and power capability of Li-ion cells to design and fabricate high energy Li-ion cells that can be discharged at very high rates. Figure 4 shows measured performance data of a 1.1 Ah CAM-7/silicon-anode based cell that can support > 270 Wh/kg at a 5C rate. A photograph of the cell is shown in Figure 5. In this presentation, we will further discuss the role of inactive components and cell designs on the rate and power capability of Li-ion cells. Using data comparing the rate capability of different cathode active materials, we will discuss the implications of our findings for research aimed at understanding the power and rate capability of active materials. We will show how judicious selection of test conditions can prevent erroneous conclusions of active material rate capability. We will provide coin cell design guidelines for measuring the rate capabilities of active materials. Finally, we will describe the key design details of the CAM-7/silicon cells that enable achieving this very high specific energy at 5C discharge rate. Figure 1

Engineering Sensorial Delay to Control Phototaxis and Emergent Collective Behaviors

Collective motions emerging from the interaction of autonomous mobile individuals play a key role in many phenomena, from the growth of bacterial colonies to the coordination of robotic swarms. For these collective behaviours to take hold, the individuals must be able to emit, sense and react to signals. When dealing with simple organisms and robots, these signals are necessarily very elementary, e.g. a cell might signal its presence by releasing chemicals and a robot by shining light. An additional challenge arises because the motion of the individuals is often noisy, e.g. the orientation of cells can be altered by Brownian motion and that of robots by an uneven terrain. Therefore, the emphasis is on achieving complex and tunable behaviors from simple autonomous agents communicating with each other in robust ways. Here, we show that the delay between sensing and reacting to a signal can determine the individual and collective long-term behavior of autonomous agents whose motion is intrinsically noisy. We experimentally demonstrate that the collective behaviour of a group of phototactic robots capable of emitting a radially decaying light field can be tuned from segregation to aggregation and clustering by controlling the delay with which they change their propulsion speed in response to the light intensity they measure. We track this transition to the underlying dynamics of this system, in particular, to the ratio between the robots' sensorial delay time and the characteristic time of the robots' random reorientation. Supported by numerics, we discuss how the same mechanism can be applied to control active agents, e.g. airborne drones, moving in a three-dimensional space.

Robot ethics and law

Purpose – This is the second part of a two-part paper which aims to provide an insight into the ethical and legal issues surrounding certain classes of robot. This part is concerned with law. Design/methodology/approach – Following an introduction, this paper first describes the European RoboLaw project and then considers legal issues and activities relating to civilian airborne drones, driverless road vehicles and assistive robots. It concludes with a short discussion. Findings – The legal issues associated with many classes of robot are the topic of much debate, and efforts are underway to create appropriate legislative frameworks. A project is presently seeking to create a framework for the development of a Europe-wide “Robolaw” and in certain cases, laws are already being formulated to accommodate recent robotic developments. These deliberations are rapidly gaining pace and are now also considering future generations of highly autonomous and intelligent robots. Originality/value – This paper provides an insight into the highly topical and complex issue of robot law.

Strategy of Air Supremacy and Defense for a Quick End to Twenty- First Century Conflict – Research Required

Air supremacy including land supremacy depends on equipment and weapons researched and developed with superior engineering knowledge, design and practice. There is the analytical and computational part, as well as the essential product realization portion. Superiority in engineering knowledge and practice is requisite. There are also telltale clues from the lyrics of national anthems, which may speak of a nation’s desire for military superiority, or at least of being undaunted by military conflict to maintain a nation’s independence. Airborne drones are being recognized as being essential for air supremacy in the twenty-first century, and are the focus of this article. The technology of airborne drones rests on the shoulders of aircraft technology, on robotics and computer technology, especially at the micro-scale end of fast and powerful computers. This work is focused on the research and development required to maintain/ acquire air supremacy in this twenty-first century, so that military conflicts need not drag on. Numerous lives can be spared as a consequence.