Framework de planeamento de missões para frotas de drones interligados

The usage of aerial drones has become more popular as they also become more accessible, both in economic and usability terms. Nowadays, these vehicles can present reduced dimensions and a good cost-benefit ratio, which makes it possible for several services and applications supported by aerial drone networks to emerge. Some scenarios that benefit from the use of aerial drones are the monitoring of emergency situations and natural disasters, the patrolling of urban areas and support to police forces, and tourist applications such as the real-time video transmission of points of interest. It is common for the control of the drone to be dependent on human intervention in these situations, which requires professionals specialized in its control. However, in recent years, several solutions have emerged that enable the autonomous flight of these vehicles, minimizing manual interference. Taking into account the enormous diversity of use cases, many of the existing solutions for autonomous control focus on specific scenarios. Generic mission planning platforms also exist, but most of them only allow missions consisting of linear waypoints to be traversed. These situations translate into a mission support that is not very flexible. In this dissertation, we propose a modular infrastructure that can be used in various scenarios, enabling the autonomous control and monitoring of a fleet of aerial drones in a mission context. This platform has two main components, one integrated into the onboard computer of the vehicle, and the other one in the ground control. The former allows the communication with the flight controller so that it can collect telemetry data and send movement instructions to the drone. The latter allows to monitor this data and send the commands remotely, also enabling robust mission planning with multiple drones. A mission can be described in a script that the ground module interprets, sending the commands to the assigned vehicles. These missions can describe different paths, modifying the behaviour of the drones according to external factors, such as a sensor reading. It is also possible to define plugins to be reused in various missions, for example, by integrating an algorithm that ensures that all drones maintain connectivity. The solution was evaluated in scenarios with a single drone and with the collaboration of multiple drones. The tests were performed in a simulated environment and also in an environment with real drones. The observed behaviour is similar in both scenarios.A utilização de drones aéreos tem-se vindo a popularizar à medida que estes se tornam mais acessíveis, quer em termos económicos quer em usabilidade. Atualmente, estes veículos são capazes de apresentar dimensões reduzidas e uma boa relação de custo-benefício, o que potencia que diversos serviços e aplicações suportados por redes de drones aéreos estejam a emergir. Alguns cenários que beneficiam da utilização de drones aéreos são a monitorização de situações de emergência e catástrofes naturais, a patrulha de áreas urbanas e apoio às forças policiais e aplicações turísticas como a transmissão de vídeo em tempo real de pontos de interesse. É comum que o controlo do drone esteja dependente de intervenção humana nestas situações, o que requer profissionais especializados no seu controlo. No entanto, nos últimos anos têm surgido diversas soluções que possibilitam o vôo autónomo destes veículos, minimizando a interferência manual. Perante a enorme diversidade de casos de aplicação, muitas das soluções existentes para o controlo autónomo focam-se em cenários específicos de intervenção. Existem também plataformas de planeamento genérico de missões, mas que na sua maioria apenas permitem missões constituídas por conjuntos lineares de pontos a ser percorridos. Estas situações traduzem-se num suporte a missões que é pouco flexível. Nesta dissertação propomos uma infraestrutura modular passível de ser utilizada em cenários variados, possibilitando o controlo autónomo de uma frota de drones aéreos num contexto de missão e a sua monitorização. Esta plataforma tem dois componentes principais, um integrado no computador a bordo do veículo e o outro no controlo terrestre. O primeiro permite a comunicação com o controlador de vôo para que se possa recolher diversos dados de telemetria e enviar instruções de movimento para o drone. O segundo permite monitorizar esses dados e enviar os comandos remotamente, possibilitando também um planeamento robusto de missões com múltiplos drones. Uma missão pode ser descrita num script que o módulo terrestre interpreta, enviando os comandos para os veículos atribuídos. Estas missões podem descrever diversos caminhos, modificando o comportamento dos drones de acordo com factores externos, como a leitura de um sensor. Também é possível definir plugins para serem reutilizados em várias missões, como por exemplo, integrando um algoritmo que garante que todos os drones mantêm a conectividade. A solução foi avaliada em cenários com um único drone e com a colaboração de múltiplos drones. Os testes foram executados em ambiente simulado e também num ambiente com drones reais. O comportamento observado nas missões é semelhante em ambos os cenários.Mestrado em Engenharia de Computadores e Telemátic

Unmanned Aerial Vehicle for Internet of Everything: Opportunities and Challenges

The recent advances in information and communication technology (ICT) have further extended Internet of Things (IoT) from the sole "things" aspect to the omnipotent role of "intelligent connection of things". Meanwhile, the concept of internet of everything (IoE) is presented as such an omnipotent extension of IoT. However, the IoE realization meets critical challenges including the restricted network coverage and the limited resource of existing network technologies. Recently, Unmanned Aerial Vehicles (UAVs) have attracted significant attentions attributed to their high mobility, low cost, and flexible deployment. Thus, UAVs may potentially overcome the challenges of IoE. This article presents a comprehensive survey on opportunities and challenges of UAV-enabled IoE. We first present three critical expectations of IoE: 1) scalability requiring a scalable network architecture with ubiquitous coverage, 2) intelligence requiring a global computing plane enabling intelligent things, 3) diversity requiring provisions of diverse applications. Thereafter, we review the enabling technologies to achieve these expectations and discuss four intrinsic constraints of IoE (i.e., coverage constraint, battery constraint, computing constraint, and security issues). We then present an overview of UAVs. We next discuss the opportunities brought by UAV to IoE. Additionally, we introduce a UAV-enabled IoE (Ue-IoE) solution by exploiting UAVs's mobility, in which we show that Ue-IoE can greatly enhance the scalability, intelligence and diversity of IoE. Finally, we outline the future directions in Ue-IoE.Comment: 21 pages, 9 figure

Novel parallel approaches to efficiently solve spatial problems on heterogeneous CPU-GPU systems

Addressing this task is difficult as (i) it requires analysing large databases in a short time, and (ii) it is commonly addressed by combining different methods with complex data dependencies, making it challenging to exploit parallelism on heterogeneous CPU-GPU systems. Moreover, most efforts in this context focus on improving the accuracy of the approaches and neglect reducing the processing time—the most accurate algorithm was designed to process the fingerprints using a single thread. We developed a new methodology to address the latent fingerprint identification problem called “Asynchronous processing for Latent Fingerprint Identification” (ALFI) that speeds up processing while maintaining high accuracy. ALFI exploits all the resources of CPU-GPU systems using asynchronous processing and fine-coarse parallelism to analyse massive fingerprint databases. We assessed the performance of ALFI on Linux and Windows operating systems using the well-known NIST/FVC databases. Experimental results revealed that ALFI is on average 22x faster than the state-of-the-art identification algorithm, reaching a speed-up of 44.7x for the best-studied case. In terrain analysis, Digital Elevation Models (DEMs) are relevant datasets used as input to those algorithms that typically sweep the terrain to analyse its main topological features such as visibility, elevation, and slope. The most challenging computation related to this topic is the total viewshed problem. It involves computing the viewshed—the visible area of the terrain—for each of the points in the DEM. The algorithms intended to solve this problem require many memory accesses to 2D arrays, which, despite being regular, lead to poor data locality in memory. We proposed a methodology called “skewed Digital Elevation Model” (sDEM) that substantially improves the locality of memory accesses and exploits the inherent parallelism of rotational sweep-based algorithms. Particularly, sDEM applies a data relocation technique before accessing the memory and computing the viewshed, thus significantly reducing the execution time. Different implementations are provided for single-core, multi-core, single-GPU, and multi-GPU platforms. We carried out two experiments to compare sDEM with (i) the most used geographic information systems (GIS) software and (ii) the state-of-the-art algorithm for solving the total viewshed problem. In the first experiment, sDEM results on average 8.8x faster than current GIS software, despite considering only a few points because of the limitations of the GIS software. In the second experiment, sDEM is 827.3x faster than the state-of-the-art algorithm considering the best case. The use of Unmanned Aerial Vehicles (UAVs) with multiple onboard sensors has grown enormously in tasks involving terrain coverage, such as environmental and civil monitoring, disaster management, and forest fire fighting. Many of these tasks require a quick and early response, which makes maximising the land covered from the flight path an essential goal, especially when the area to be monitored is irregular, large, and includes many blind spots. In this regard, state-of-the-art total viewshed algorithms can help analyse large areas and find new paths providing all-round visibility. We designed a new heuristic called “Visibility-based Path Planning” (VPP) to solve the path planning problem in large areas based on a thorough visibility analysis. VPP generates flyable paths that provide high visual coverage to monitor forest regions using the onboard camera of a single UAV. For this purpose, the hidden areas of the target territory are identified and considered when generating the path. Simulation results showed that VPP covers up to 98.7% of the Montes de Malaga Natural Park and 94.5% of the Sierra de las Nieves National Park, both located in the province of Malaga (Spain). In addition, a real flight test confirmed the high visibility achieved using VPP. Our methodology and analysis can be easily applied to enhance monitoring in other large outdoor areas.In recent years, approaches that seek to extract valuable information from large datasets have become particularly relevant in today's society. In this category, we can highlight those problems that comprise data analysis distributed across two-dimensional scenarios called spatial problems. These usually involve processing (i) a series of features distributed across a given plane or (ii) a matrix of values where each cell corresponds to a point on the plane. Therefore, we can see the open-ended and complex nature of spatial problems, but it also leaves room for imagination to be applied in the search for new solutions. One of the main complications we encounter when dealing with spatial problems is that they are very computationally intensive, typically taking a long time to produce the desired result. This drawback is also an opportunity to use heterogeneous systems to address spatial problems more efficiently. Heterogeneous systems give the developer greater freedom to speed up suitable algorithms by increasing the parallel programming options available, making it possible for different parts of a program to run on the dedicated hardware that suits them best. Several of the spatial problems that have not been optimised for heterogeneous systems cover very diverse areas that seem vastly different at first sight. However, they are closely related due to common data processing requirements, making them suitable for using dedicated hardware. In particular, this thesis provides new parallel approaches to tackle the following three crucial spatial problems: latent fingerprint identification, total viewshed computation, and path planning based on maximising visibility in large regions. Latent fingerprint identification is one of the essential identification procedures in criminal investigations. Addressing this task is difficult as (i) it requires analysing large databases in a short time, and (ii) it is commonly addressed by combining different methods with complex data dependencies, making it challenging to exploit parallelism on heterogeneous CPU-GPU systems. Moreover, most efforts in this context focus on improving the accuracy of the approaches and neglect reducing the processing time—the most accurate algorithm was designed to process the fingerprints using a single thread. We developed a new methodology to address the latent fingerprint identification problem called “Asynchronous processing for Latent Fingerprint Identification” (ALFI) that speeds up processing while maintaining high accuracy. ALFI exploits all the resources of CPU-GPU systems using asynchronous processing and fine-coarse parallelism to analyse massive fingerprint databases. We assessed the performance of ALFI on Linux and Windows operating systems using the well-known NIST/FVC databases. Experimental results revealed that ALFI is on average 22x faster than the state-of-the-art identification algorithm, reaching a speed-up of 44.7x for the best-studied case. In terrain analysis, Digital Elevation Models (DEMs) are relevant datasets used as input to those algorithms that typically sweep the terrain to analyse its main topological features such as visibility, elevation, and slope. The most challenging computation related to this topic is the total viewshed problem. It involves computing the viewshed—the visible area of the terrain—for each of the points in the DEM. The algorithms intended to solve this problem require many memory accesses to 2D arrays, which, despite being regular, lead to poor data locality in memory. We proposed a methodology called “skewed Digital Elevation Model” (sDEM) that substantially improves the locality of memory accesses and exploits the inherent parallelism of rotational sweep-based algorithms. Particularly, sDEM applies a data relocation technique before accessing the memory and computing the viewshed, thus significantly reducing the execution time. Different implementations are provided for single-core, multi-core, single-GPU, and multi-GPU platforms. We carried out two experiments to compare sDEM with (i) the most used geographic information systems (GIS) software and (ii) the state-of-the-art algorithm for solving the total viewshed problem. In the first experiment, sDEM results on average 8.8x faster than current GIS software, despite considering only a few points because of the limitations of the GIS software. In the second experiment, sDEM is 827.3x faster than the state-of-the-art algorithm considering the best case. The use of Unmanned Aerial Vehicles (UAVs) with multiple onboard sensors has grown enormously in tasks involving terrain coverage, such as environmental and civil monitoring, disaster management, and forest fire fighting. Many of these tasks require a quick and early response, which makes maximising the land covered from the flight path an essential goal, especially when the area to be monitored is irregular, large, and includes many blind spots. In this regard, state-of-the-art total viewshed algorithms can help analyse large areas and find new paths providing all-round visibility. We designed a new heuristic called “Visibility-based Path Planning” (VPP) to solve the path planning problem in large areas based on a thorough visibility analysis. VPP generates flyable paths that provide high visual coverage to monitor forest regions using the onboard camera of a single UAV. For this purpose, the hidden areas of the target territory are identified and considered when generating the path. Simulation results showed that VPP covers up to 98.7% of the Montes de Malaga Natural Park and 94.5% of the Sierra de las Nieves National Park, both located in the province of Malaga (Spain). In addition, a real flight test confirmed the high visibility achieved using VPP. Our methodology and analysis can be easily applied to enhance monitoring in other large outdoor areas

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

THE AIRCRAFT MAINTENANCE ENGINEER COMPETENCE WITHIN THE CONTEXT OF AVIATION SAFETY REGULATIONS

This thesis is intended to serve as a guide for operation of a flight safety function within international safety organizations. This paper is specifically focused on the impact of European Aviation Safety Agency (EASA) Regulations as they are strongly applied to Aircraft Maintenance. The paper is intended on responsibilities for releasing Aircraft Maintenance Engineer License to sign off aircraft for flight. It also includes guidance to competency requirements of the Aircraft Maintenance Engineer

Dipterocarps protected by Jering local wisdom in Jering Menduyung Nature Recreational Park, Bangka Island, Indonesia

Apart of the oil palm plantation expansion, the Jering Menduyung Nature Recreational Park has relatively diverse plants. The 3,538 ha park is located at the north west of Bangka Island, Indonesia. The minimum species-area curve was 0.82 ha which is just below Dalil conservation forest that is 1.2 ha, but it is much higher than measurements of several secondary forests in the Island that are 0.2 ha. The plot is inhabited by more than 50 plant species. Of 22 tree species, there are 40 individual poles with the average diameter of 15.3 cm, and 64 individual trees with the average diameter of 48.9 cm. The density of Dipterocarpus grandiflorus (Blanco) Blanco or kruing, is 20.7 individual/ha with the diameter ranges of 12.1 – 212.7 cm or with the average diameter of 69.0 cm. The relatively intact park is supported by the local wisdom of Jering tribe, one of indigenous tribes in the island. People has regulated in cutting trees especially in the cape. The conservation agency designates the park as one of the kruing propagules sources in the province. The growing oil palm plantation and the less adoption of local wisdom among the youth is a challenge to forest conservation in the province where tin mining activities have been the economic driver for decades. More socialization from the conservation agency and the involvement of university students in raising environmental awareness is important to be done

IKUWA6. Shared Heritage

Celebrating the theme ‘Shared heritage’, IKUWA6 (the 6th International Congress for Underwater Archaeology), was the first such major conference to be held in the Asia-Pacific region, and the first IKUWA meeting hosted outside Europe since the organisation’s inception in Germany in the 1990s. A primary objective of holding IKUWA6 in Australia was to give greater voice to practitioners and emerging researchers across the Asia and Pacific regions who are often not well represented in northern hemisphere scientific gatherings of this scale; and, to focus on the areas of overlap in our mutual heritage, techniques and technology. Drawing together peer-reviewed presentations by delegates from across the world who converged in Fremantle in 2016 to participate, this volume covers a stimulating diversity of themes and niche topics of value to maritime archaeology practitioners, researchers, students, historians and museum professionals across the world