Aerial Drones Research Articles

Next-Gen Intelligent Situational Awareness Systems for Maritime Surveillance and Autonomous Navigation [Point of View

Today, the maritime domain is at the cusp of a new era, driven by technological advances in automation, robotics, multisensor perception, and artificial intelligence (AI), together with digitalization and connectivity. Smart ship infrastructure and technology, remotely controlled and autonomous ship operation to improve safety, security, cost efficiency, and sustainability are the future of maritime transportation <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , representing now the engine of 90% of global trade <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> . Ships will soon benefit from recent developments in sensors, telecommunications, and computing technologies to turn the smart shipping revolution into reality <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> and <xref ref-type="bibr" rid="ref4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</xref> , as it has already happened for autonomous vehicles such as driverless cars, aerial drones, unmanned (or remotely piloted) aircraft, and underwater vehicles.

Saildrones and Snotbots in the Blue Anthropocene: Sensing technologies, multispecies intimacies, and scientific storying

Drones or unpersonned vehicles are mobile sensing technologies that collapse space and enhance proximity between scientists and marine species. As such, they improve the collection of biological data – images, migration maps, and fluid samples, for example. But while the drone’s benefits to oceanography are apparent, it is less clear what marine species receive for their unintentional participation in data collection. Building on ethnography, piloting experiments, interviews, and scrutiny of public blogs and scientific texts, this article documents two cases of drone oceanography, interrogates the multispecies intimacies they forge and considers what scientists return to marine animals in exchange for their biological data. The US National Oceanic and Atmospheric Administration directs ocean-surface Saildrones to follow northern fur seals in the Bering Sea, and Ocean Alliance, a not-for-profit research organization, collects microbes from cetaceans by flying aerial drones, or Snotbots, through their exhale. With the aim of generating more equitable reciprocities in waters that are surveyed by drones and increasingly challenging to live within, this article offers storying, or the building of existential narratives that support conservation through public engagement, as a way of forging multispecies reciprocities in the Blue Anthropocene – an era marked by existential urgencies, technological materialities, and elemental constraints.

Self-co-ordination algorithm (SCA) for multi-UAV systems using fair scheduling queue

PurposeData traffic through wireless communication is significantly increasing, resulting in the frequency of streaming applications as various formats and the evolution of the Internet of Things (IoT), such as virtual reality, edge device based transportation and surveillance systems. Growth in kind of applications resulted in increasing the scope of wireless communication and allocating a spectrum, as well as methods to decrease the intervention between nearby-located wireless links functioning on the same spectrum bands and hence to proliferation for the spectral efficiency. Recent advancement in drone technology has evolved quickly leading on board sensors with increased energy, storage, communication and processing capabilities. In future, the drone sensor networks will be more common and energy utilization will play a crucial role to maintain a fully functional network for the longest period of time. Envisioning the aerial drone network, this study proposes a robust high level design of algorithms for the drones (group coordination). The proposed design is validated with two algorithms using multiple drones consisting of various on-board sensors. In addition, this paper also discusses the challenges involved in designing solutions. The result obtained through proposed method outperforms the traditional techniques with the transfer rate of more than 3 MB for data transfer in the drone with coordinationDesign/methodology/approachFair Scheduling Algorithm (FSA) using a queue is a distributed slot assignment algorithm. The FSA executes in rounds. The duration of each round is dynamic based upon the delay in the network. FSA prevents the collision by ensuring that none of the neighboring node gets the same slot. Nodes (Arivudainambi et al., 2019) which are separated by two or more hopes can get assigned in the same slot, thereby preventing the collision. To achieve fairness at the scheduling level, the FSA maintains four different states for each node as IDLE, REQUEST, GRANT and RELEASE.FindingsA multi-unmanned aerial vehicle (UAV) system can operate in both centralized and decentralized manner. In a centralized system, the ground control system will take care of drone data collection, decisions on navigation, task updation, etc. In a decentralized system, the UAVs are unambiguously collaborating on various levels as mentioned in the centralized system to achieve the goal which is represented in Figure 2.Research limitations/implicationsHowever, the multi-UAVs are context aware in situations such as environmental observation, UAV–UAV communication and decision-making. Independent of whether operation is centralized or decentralized, this study relates the goals of the multi-UAVs are sensing, communication and coordination among other UAVs, etc. Figure 3 shows overall system architecture.Practical implicationsThe individual events attempts in the UAV’s execution are required to complete the mission in superlative manner which affects in every multi UAV system. This multi UAV systems need to take a steady resolute on what way UAV has to travel and what they need to complete to face the critical situations in changing of environments with the uncertain information. This coordination algorithm has certain dimensions including events that they needs to resolute on, the information that they used to make a resolution, the resolute making algorithm, the degree of decentralization. In multi UAV systems, the coordinated events ranges from lower motion level.Originality/valueThis study has proposed a novel self-organizing coordination algorithm for multi-UAV systems. Further, the experimental results also confirm that is robust to form network at ease. The testbed for this simulation to sensing, communication, evaluation and networking. The algorithm coordination has to testbed with multi UAVs systems. The two scheduling techniques has been used to transfer the packets using done network. The self-organizing algorithm (SOA) with fair scheduling queue outperforms the weighted queue scheduling in the transfer rate with less loss and time lag. The results obtained through from Figure 10 clearly indicates that the fair queue scheduling with SOA have several advantages over weighted fair queue in different parameters.

A PERFORMANCE ANALYSIS ON DRONE LOCO POSITIONING SYSTEM FOR TWO-WAY RANGING PROTOCOL

Localization is vital in UAV operation as it monitors the position of each drone in a workspace. Existing localization techniques such as GPS are limited for outdoor implementations and cannot be implemented inside closed spaces or GPS denied areas. To address this concern localization techniques, such as vision systems and radio systems, are developed. The drawback of vision systems is the cost of implementation as the system usually requires multiple cameras strategically positioned around the experimental space to monitor the aerial drone’s position and orientation. Radio localization, on the other hand, is a cheaper alternative for indoor localization as it requires only a set of anchor and tags that communicates through a certain radio frequency; however, experimental setups and materials on this localization technique is limited at this time. This paper offers an analysis of the performance of the loco positioning system, a form of radio localization, through varying configurations for swarm drone applications. The Loco Positioning System possesses two protocols; and this paper focuses on the Two-Way Ranging protocol. The study presents different setup configurations governed by 2 parameters; number of anchors used, and the distance set between anchors, and their corresponding performances. Data showed that an increase in anchor count from 3 to 6 decreases error from 25.96% to 8.45%, and that decreasing the distance between anchors 0.6 m to 1 m would give a minimal increase in error. Users may use these performance reports to determine their ideal setup based on the mentioned parameters.

IoT Enabled Deep Learning Based Framework for Multiple Object Detection in Remote Sensing Images

Advanced collaborative and communication technologies play a significant role in intelligent services and applications, including artificial intelligence, Internet of Things (IoT), remote sensing, robotics, future generation wireless, and aerial access networks. These technologies improve connectivity, energy efficiency, and quality of services of various smart city applications, particularly in transportation, monitoring, healthcare, public services, and surveillance. A large amount of data can be obtained by IoT systems and then examined by deep learning methods for various applications, e.g., object detection or recognition. However, it is a challenging and complex task in smart remote monitoring applications (aerial and drone). Nevertheless, it has gained special consideration in recent years and has performed a pivotal role in different control and monitoring applications. This article presents an IoT-enabled smart surveillance solution for multiple object detection through segmentation. In particular, we aim to provide the concept of collaborative drones, deep learning, and IoT for improving surveillance applications in smart cities. We present an artificial intelligence-based system using the deep learning based segmentation model PSPNet (Pyramid Scene Parsing Network) for segmenting multiple objects. We used an aerial drone data set, implemented data augmentation techniques, and leveraged deep transfer learning to boost the system’s performance. We investigate and analyze the performance of the segmentation paradigm with different CNN (Convolution Neural Network) based architectures. The experimental results illustrate that data augmentation enhances the system’s performance by producing good accuracy results of multiple object segmentation. The accuracy of the developed system is 92% with VGG-16 (Visual Geometry Group), 93% with ResNet-50 (Residual Neural Network), and 95% with MobileNet.

Mission Planning for Low Altitude Aerial Drones during Water Sampling

Mission planning for small uncrewed aerial systems (sUAS) as a platform for remote sensors goes beyond the traditional issues of selecting a sensor, flying altitude/speed, spatial resolution, and the date/time of operation. Unlike purchasing or contracting imagery collections from traditional satellite or manned airborne systems, the sUAS operator must carefully select launching, landing, and flight paths that meet both the needs of the remote sensing collection and the regulatory requirements of federal, state, and local regulations. Mission planning for aerial drones must consider temporal and geographic changes in the environment, such as local weather conditions or changing tidal height. One key aspect of aerial drone missions is the visibility of the aircraft and communication with the aircraft. In this research, a visibility model for low-altitude aerial drone operations was designed using a GIS-based framework supported by high spatial resolution LiDAR data. In the example study, the geographic positions of the visibility of an aerial drone used for water sampling at low altitudes (e.g., 2 m above ground level) were modeled at different levels of tidal height. Using geospatial data for a test-case environment at the Winyah Bay estuarine environment in South Carolina, we demonstrate the utility, challenges, and solutions for determining the visibility of a very low-altitude aerial drone used in water sampling.

An automated work-flow for pinniped surveys: A new tool for monitoring population dynamics

Detecting changes in population trends depends on the accuracy of estimated mean population growth rates and thus the quality of input data. However, monitoring wildlife populations poses economic and logistic challenges especially in complex and remote habitats. Declines in wildlife populations can remain undetected for years unless effective monitoring techniques are developed, guiding appropriate management actions. We developed an automated survey workflow using unmanned aerial vehicles (drones) to quantify the number and size of individual animals, using the well-studied Scandinavian harbour seal (Phoca vitulina) as a model species. We compared ground-based counts using telescopes with manual flights, using a zoom photo/video, and pre-programmed flights producing orthomosaic photo maps. We used machine learning to identify and count both pups and older seals and we present a new method for measuring body size automatically. We evaluate the population’s reproductive success using drone data, historical counts and predictions from a Leslie matrix population model. The most accurate and time-efficient results were achieved by performing pre-programmed flights where individual seals are identified by machine learning and their body sizes are measured automatically. The accuracy of the machine learning detector was 95–97% and the classification error was 4.6 ± 2.9 for pups and 3.1 ± 2.1 for older seals during good light conditions. There was a clear distinction between the body sizes of pups and older seals during breeding time. We estimated 320 pups in the breeding season 2021 with the drone, which is well beyond the expected number, based on historical data on pup production. The new high quality data from the drone survey confirms earlier indications of a deteriorating reproductive rate in this important harbour seal colony. We show that aerial drones and machine learning are powerful tools for monitoring wildlife in inaccessible areas which can be used to assess annual recruitment and seasonal variations in body condition.

A Mean-Field Game Control for Large-Scale Swarm Formation Flight in Dense Environments.

As an important part of cyberphysical systems (CPSs), multiple aerial drone systems are widely used in various scenarios, and research scenarios are becoming increasingly complex. However, planning strategies for the formation flying of aerial swarms in dense environments typically lack the capability of large-scale breakthrough because the amount of communication and computation required for swarm control grows exponentially with scale. To address this deficiency, we present a mean-field game (MFG) control-based method that ensures collision-free trajectory generation for the formation flight of a large-scale swarm. In this paper, two types of differentiable mean-field terms are proposed to quantify the overall similarity distance between large-scale 3-D formations and the potential energy value of dense 3-D obstacles, respectively. We then formulate these two terms into a mean-field game control framework, which minimizes energy cost, formation similarity error, and collision penalty under the dynamical constraints, so as to achieve spatiotemporal planning for the desired trajectory. The classical task of a distributed large-scale aerial swarm system is simulated by numerical examples, and the feasibility and effectiveness of our method are verified and analyzed. The comparison with baseline methods shows the advanced nature of our method.

Aerial Observations and Numerical Simulations Confirm Density‐Driven Streamwise Vortices at a River Confluence

AbstractWhen rivers collide, complex three‐dimensional coherent flow structures are generated along the confluence's mixing interface. These structures mix streamborne pollutants and suspended sediment and have considerable bearing on the morphology and habitat quality of the postconfluent reach. A particular structure of interest—streamwise orientated vortices (SOVs)—were first detected in numerical simulations to form in pairs, one on each side of the mixing interface rotating in the opposite sense of the other. Since, it has proven difficult to detect SOVs in situ with conventional pointwise velocimetry instrumentation. Despite the lack of clear evidence to confirm their existence, SOVs are nevertheless considered important drivers of mixing and sediment transport processes at confluences. Additionally, their causal mechanisms are not fully known which hinders a complete conceptual understanding of these processes. To address these gaps, we analyze observations of strongly coherent SOVs filmed in aerial drone video of a mesoscale confluence with a stark turbidity contrast between its tributaries. Eddy‐resolved modeling demonstrates the SOVs' dynamics could only be accurately reproduced when a density difference (Δρ) was imposed between the tributaries (Δρ = 0.5 kg/m3)—providing compelling evidence the observed SOVs are indeed a novel density‐driven class of SOV. This work confirms that SOVs exist, expands understanding of their generative processes and highlights the important role of small density gradients (e.g., ≤0.5 kg/m3) on river confluence hydrodynamics.

Drone-Based Sensor Information Gathering System With Beam-Rotation Forward-Scattering Communications and Wireless Power Transfer

In this work, we propose a drone-based sensor information gathering system that utilizes an aerial drone to wirelessly transfer an electromagnetic (EM) wave signal toward a battery-less forward-scatter tag device with multiple antennas. The proposed tag device is designed to harvest the EM signal from the drone as a source of power, and at the same time to reuse the EM signal for the data transmission. We propose a beam-rotation forward-scattering algorithm for the data transmission, which creates the main beam rotating along the azimuth plane. In the proposed algorithm, data are transmitted from the tag device to the tag reader by mapping each bit of information to varying beam rotation speeds. A unique hardware design of the forward-scatter tag device is proposed in this article, and it has been fabricated and implemented for wireless power transfer (WPT) and data transmission experiments. By experiment, we have verified that power is successfully harvested by the proposed tag device, with the average power conversion efficiency rated at 57%. The proposed tag device is able to transmit the sensor data to the tag readers without the knowledge of the location of the tag readers. We have tested the proposed beam-rotation forward-scattering communication at up to 500 m communication distance and shown that the sensor data can successfully be delivered.

More aggressive sea turtles win fights over foraging resources independent of body size and years of presence

Interactions among individuals of the same species inform us about resource value and sociality; however, for species that are difficult to observe directly, these behaviours are potentially missed or misinterpreted via remote monitoring approaches. Here, we investigated how agonistic interactions influenced temporal and spatial variation in the social structuring of loggerhead sea turtles, Caretta caretta , foraging on sponges at a reef (Zakynthos, Greece) over multiple years. We directly observed individuals via snorkel surveys (with photoidentification and videos) supported by aerial drone surveys. Most turtles were immature loggerheads, of which half were detected in more than 1 year. Individual loggerheads occupied distinct (very small) patches on the reef, with low overlap when foraging. Interactions generally occurred when loggerheads were adjacent to, or passing through, patches occupied by other individuals. Interactions between loggerheads represented an extremely small fraction of the activity budget, but potentially consumed two to three times more energy than swimming and foraging. Escalation of aggression, unexpectedly, skipped the passive (evaluation) phase in half of interactions (starting with biting and chasing), with contests being primarily won by more aggressive individuals, independent of body size and duration of presence (i.e. reef occupancy in years). Distinct hierarchical structuring was detected based on win outcomes and spatial area use within each year; however, the number and combination of turtles frequenting the reef varied across years, with individuals only dominating for 2–3 years before departing (or returning intermittently) and being replaced. The unexpected fight dynamics and regularly changing hierarchies indicate that individuals perceived the resource value of sponges differently, which was attributed to differences in their ability to assess resource abundance and/or availability of resources at alternative sites. Our results contribute to the emerging field of sociality in reptiles. • Interactions represented 1% of the activity budget of foraging loggerhead turtles. • Interactions appeared more energetically costly than swimming or foraging. • Interactions were immediately aggressive (no passive evaluation) in 48% of cases. • Winners were the most aggressive, independent of body size or years of presence. • Individuals remained dominant for 2–3 years only before leaving or, rarely, returning.

Time optimal control of a non-linear surface vehicle subject to disturbances

The problem of an autonomous agent moving on a planar surface, such as an aerial drone or a small naval vessel can be treated as navigation between a series of points. While nominally the movement between each pair of points can be treated as a 1D projection of the movement on the vector connecting the two points, in the presence of disturbances, the full problem on the plane must be considered. The time optimal solution is now dependent on the value and direction of the disturbance which in this paper is assumed to be a constant inertial velocity of the medium (wind or current, respectively). We address the minimum time problem of movement on a 2D plane with quadratic drag, under norm state (inertial vessel velocity), and norm control (acceleration) constraints. The structure and properties of the optimal solution are found and analyzed, utilizing the Pontryagin Maximum Principle (PMP) with control and state constraints. Simulations supporting the results are provided and compared with those of the open-source academic optimal control solver Falcon.m.

Autonomous Navigation of an Aerial Drone to Observe a Group of Wild Animals With Reduced Visual Disturbance

Protection of wild animals relies on understanding the interaction between the animals and their environment. With the ability to rapidly access rugged areas, aerial monitoring by drones is fast becoming a viable tool for ecologists to monitor wild animals. Unfortunately, this approach results in significant disturbance to different species of wild animals. Inspired by motion camouflage, this article explores a navigation method for a drone to covertly observe a group of animals and their habitat. Unlike existing motion-camouflage navigation approaches that deceive a single target, we introduce a sliding-mode-based method that reactively navigates the drone to induce less optical flow on multiple targets’ visual system. The proposed method is computationally simple and suitable for a drone to closely observe a group of moving animals with reduced visual disturbance. Computer simulations are conducted to demonstrate the performance of the proposed method.

Aerial Drones Reveal the Dynamic Structuring of Sea Turtle Breeding Aggregations and Minimum Survey Effort Required to Capture Climatic and Sex-Specific Effects

Quantifying how animals use key habitats and resources for their survival allows managers to optimise conservation planning; however, obtaining representative sample sizes of wildlife distributions in both time and space is challenging, particularly in the marine environment. Here, we used unoccupied aircraft systems (UASs) to evaluate temporal and spatial variation in the distribution of loggerhead sea turtles (Caretta caretta) at two high-density breeding aggregations in the Mediterranean, and the effect of varying sample size and survey frequency. In May–June of 2017 to 2019, we conducted 69 surveys, assimilating 10,075 inwater turtle records at the two sites. Optimal time interval between surveys to capture the dynamics of aggregations over the breeding period was &amp;lt;2-week intervals and &amp;gt;500 turtles (from the combined surveys). This minimum threshold was attributed to the core-area use of female turtles shifting across surveys in relation to wind direction to access warmer nearshore waters and male presence. Males were more widely distributed within aggregations than females, particularly in May when mating encounters were high. Most males were recorded swimming and oriented parallel to shore, likely to enhance encounter rates with females. In contrast, most females were generally stationary (resting on the seabed or basking), likely to conserve energy for reproduction, with orientation appearing to shift in relation to male numbers at the breeding area. Thus, by identifying the main factors regulating the movement and distribution of animals, appropriate survey intervals can be selected for appropriate home range analyses. Our study demonstrates the versatility of UASs to capture the fine-scale dynamics of wildlife aggregations and associated factors, which is important for implementing effective conservation.

Comparison of drone and vessel-based collection of microbiological water samples in marine environments

Many water quality metrics cannot be measured in situ and require collection of a physical sample for laboratory analysis. This includes microbiological samples for detection of fecal coliform bacteria in marine and freshwater systems which are a critical component of food safety programs for human consumption of bivalve shellfish worldwide. Water sample collection programs are typically vessel-based which can be time and resource intensive. In Canada, the Canadian Shellfish Sanitation Program aims to avoid consumption of contaminated molluscan bivalves by monitoring fecal coliform bacteria through vessel-based water sample collection. Uncrewed aerial vehicles or drones are becoming more commonly used for water sample collection given their relatively low cost but are rarely used to support microbiological analyses. A prerequisite for the acceptance of a new collection method for a regulatory program is to determine if the method of sample collection affects results. To assess this potential, we designed, developed, and tested a sampling device attached to the underside of a drone to collect water samples for bacteriological analysis. Drone and vessel-based samples were collected in the same location, at the same 20-cm depth, within a minute apart, at ten different geographic locations in coastal Nova Scotia waters to compare fecal coliform counts. Bacterial count estimates obtained from drone-collected samples were not significantly different than estimates obtained from vessel-collected samples (p < 0.5). Results from this study suggest novel water sampling techniques using drones could supplement or replace traditional vessel-based sampling methods.

Sensing spectrum sharing based massive MIMO radar for drone tracking and interception.

Radar sensors are becoming crucial for environmental perception in a world with the tremendous growth of unmanned aerial vehicles (UAVs) or drones. When public safety is a concern, the localization of drones are of great significance. However, a drone used for a wrong motive can cause a serious problem for the environment and public safety, given the fact that the dynamic movement of a drone’s emission signal and location tracking is different from existing positioning. This study proposes a safety zone characterized by the presence of N radars sensors with a goal to track and destabilized rogue drones attending to penetrate safety zones (stadium and school). Specifically, a new joint estimation based on a Gaussian filter has been introduced for spectrum sharing and detection awareness. The profit of this novel sensing method can be clearly seen when the two joint hidden states are taken into consideration. Therefore, the drone’s emission state is analyzed by estimating its movement jointly. Considering the drone’s unknown states and actual positioning, an algorithm is developed based on dynamic states space model. Where Bernoulli filter model is designed to estimate recursively the unknown stages of the drone and its changing location based on time. Meanwhile a power control acted from the radar to the targeted drones so that rogue drones are optimally tracked and destabilized over time. Furthermore, an expanding mechanism has been generated to accurately track the drone and enhance detection. A thoughtful result of the experimentation shows clearly that, even when the drone is moving, spectral detection can be performed accurately by chasing its positions. Its demonstrates at 90% of credibility that the original signal has a direct effect on the propagated signal. Therefore, the magnitude of the Doppler shift increases with frequency. And the clue of its positioning can be used for cognitive radio optimization.

The challenges and opportunities of using small drones to monitor fishing activities in a marine protected area

AbstractFishing monitoring to gather fishery dynamic data is usually land‐based or carried from onboard inspections. However, direct georeferencing of fishing activities using aerial drones is a versatile and easy option that can potentially improve fishery studies, especially in Marine Protected Areas. We investigated the utility of aerial images to understand spatial displacement and fishing territoriality in a Brazilian MPA system under risk of deregulation. Survey flights were conducted in the multiple use area where fishing is allowed in the Fernando de Noronha Archipelago, located in the South Atlantic Ocean and considered one of the main ecotourism destinations and marine conservation sites in Brazil. An aircraft was used to map and detect several aspects of fishing activities and shallow seabed characteristics to demonstrate its utility for fishery management. We recorded 89 fishing activities in the marine area and 75 shore fishers on beaches. Results documented areas most intensively targeted by fishers, and operational aspects of fishing effort, both onboard and shoreline activities targeting shoals. Enforcement and monitoring efforts must be strengthened to assess the impact of expanded fishing activity in this marine sanctuary, mainly due to fading conservation rules imposed by top‐down political decisions. This aerial approach demonstrates the utility of quantifying and mapping fishing activities to assist both MPA and fisheries management aligned to benchmarks for biodiversity conservation and human‐based demands.

INFLUENCE OF MIDDLE EAST ARMED CONFLICTS INTO BATTLEFIELD CHANGES

Wars conducted in the Middle East after the WWII significantly affected the course of military strategy, and combat tactics, and military technology. Presented paper describes circumstances of local wars and military conflicts in the Middle East and their influence on development of warfare art and technology. The wars with Israel which were lost by the Arabic countries in 20th century affected development of military strategy and tactics of using the armoured weapons and air forces. They contributed significantly to development of the intelligence, and antiaircraft combat assets, and antitank weapons. In consecutive wars the Arabic countries were not able to coordinate military operations, what gave possibility for the Israel army to successively defeat armies of particular countries. Military conflicts between Israel and Hamas and Hezbollah, and suffered losses, caused the development of new revolutionary combat assets such as combat vehicles active self-defence systems (Trophy), or antimissile/antiaircraft systems (Irone Dome). Unmanned aerial vehicles (UAV), drones, and precise weapons were widely used. Reconnaissance and military intelligence affected significantly the results of military operations. Palestinian and Lebanese organisations counterweighed the advantages of Israelian army by systems of fortifications, tunnels, and hidden fire positions. Israelian army implemented combat vehicles in urban area fights. They proved to be efficient what denied opinions about uselessness of armoured weapons at urban fights, or even the tanks in the 21st century. The war in Syria indicated the efficient use of combined combat tactics, conventional military operations, and guerrilla and terrorist methods and actions.The high morale represented by Hezbollah and ISIS fighters proved to be an essential component of military operations. Risking own life at fighting the enemies made great impression on them, and disorganised the defence, and provided dazzling successes in the battlefield. Aviation played important role in all operations fighting the forces of Arabic countries, Hamas, Hezbollah, or Islamic State (IS).

Visualizing the Anthropocene from Above

We created the Indiana Anthropocene project not only because we reside in this state, but also in order to demonstrate the scope and process of the new epoch in a familiar and defined location. This aerial photography project captures features of the Anthropocene within a regional bounded space, thus collapsing the global into the local. In other words, it provides a visual conceptualization of the geography we live in as a microcosm of the global stage. Furthermore, aerial drone photographs provide vantage points and perspectives of our world that can enhance our perception of society. It allows us to see and connect large-scale processes that otherwise remain hidden from the ground. Images from the sky piece together a complex visual socio-ecological story of how we interact with the environment on immense landscapes.

A Time-Efficient Method to Avoid Collisions for Collision Cones: An Implementation for UAVs Navigating in Dynamic Environments

This paper presents a methodology that can be used to avoid collisions of aerial drones. Even though there are many collision avoidance methods available in literature, collision cone is a proven method that can be used to predict a collision beforehand. In this research, we propose an algorithm to avoid a collision in a time-efficient manner for collision cone based aerial collision avoidance approaches. Furthermore, the paper has considered all possible scenarios including heading change, speed change and combined heading and speed change, to avoid a collision. The heading-based method was mathematically proven to be the most time-efficient method out of the three. The proposed heading-based method was compared with other work presented in the literature and validated with both simulations and experiments. A Matrice 600 Pro hexacopter is used for the collision avoidance experiments.