Architecture and Information Requirements to Assess and Predict Flight Safety Risks During Highly Autonomous Urban Flight Operations

As aviation adopts new and increasingly complex operational paradigms, vehicle types, and technologies to broaden airspace capability and efficiency, maintaining a safe system will require recognition and timely mitigation of new safety issues as they emerge and before significant consequences occur. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. In-time safety assurance comprises monitoring, assessment, and mitigation functions that proactively reduce risk in complex operational environments where the interplay of hazards may not be known (and therefore not accounted for) during design. These functions can also help to understand and predict emergent effects caused by the increased use of automation or autonomous functions that may exhibit unexpected non-deterministic behaviors. The envisioned monitoring and assessment functions can look for precursors, anomalies, and trends (PATs) by applying model-based and data-driven methods. Outputs would then drive downstream mitigation(s) if needed to reduce risk. These mitigations may be accomplished using traditional design revision processes or via operational (and sometimes automated) mechanisms. The latter refers to the in-time aspect of the system concept. This report comprises architecture and information requirements and considerations toward enabling such a capability within the domain of low altitude highly autonomous urban flight operations. This domain may span, for example, public-use surveillance missions flown by small unmanned aircraft (e.g., infrastructure inspection, facility management, emergency response, law enforcement, and/or security) to transportation missions flown by larger aircraft that may carry passengers or deliver products. Caveat: Any stated requirements in this report should be considered initial requirements that are intended to drive research and development (R&D). These initial requirements are likely to evolve based on R&D findings, refinement of operational concepts, industry advances, and new industry or regulatory policies or standards related to safety assurance

Multi-sensor data fusion techniques for RPAS detect, track and avoid

Accurate and robust tracking of objects is of growing interest amongst the computer vision scientific community. The ability of a multi-sensor system to detect and track objects, and accurately predict their future trajectory is critical in the context of mission- and safety-critical applications. Remotely Piloted Aircraft System (RPAS) are currently not equipped to routinely access all classes of airspace since certified Detect-and-Avoid (DAA) systems are yet to be developed. Such capabilities can be achieved by incorporating both cooperative and non-cooperative DAA functions, as well as providing enhanced communications, navigation and surveillance (CNS) services. DAA is highly dependent on the performance of CNS systems for Detection, Tacking and avoiding (DTA) tasks and maneuvers. In order to perform an effective detection of objects, a number of high performance, reliable and accurate avionics sensors and systems are adopted including non-cooperative sensors (visual and thermal cameras, Laser radar (LIDAR) and acoustic sensors) and cooperative systems (Automatic Dependent Surveillance-Broadcast (ADS-B) and Traffic Collision Avoidance System (TCAS)). In this paper the sensors and system information candidates are fully exploited in a Multi-Sensor Data Fusion (MSDF) architecture. An Unscented Kalman Filter (UKF) and a more advanced Particle Filter (PF) are adopted to estimate the state vector of the objects based for maneuvering and non-maneuvering DTA tasks. Furthermore, an artificial neural network is conceptualised/adopted to exploit the use of statistical learning methods, which acts to combined information obtained from the UKF and PF. After describing the MSDF architecture, the key mathematical models for data fusion are presented. Conceptual studies are carried out on visual and thermal image fusion architectures

On the Use of Unmanned Aerial Systems for Environmental Monitoring

Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small- and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air- and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challengespublishersversionPeer reviewe

Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

This work was supported by the projects: "VIRTUOUS" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2019. Ref. 872181, "SUSTAINABLE" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2020. Ref. 101007702 and the "Project of Excellence" from Junta de Andalucia 2020. Ref. P18-H0-4700. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.European Commission 101007702 872181Junta de Andalucia P18-H0-470

Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture

The use of sensors and the Internet of Things (IoT) is key to moving the world\u27s agriculture to a more productive and sustainable path. Recent advancements in IoT, Wireless Sensor Networks (WSN), and Information and Communication Technology (ICT) have the potential to address some of the environmental, economic, and technical challenges as well as opportunities in this sector. As the number of interconnected devices continues to grow, this generates more big data with multiple modalities and spatial and temporal variations. Intelligent processing and analysis of this big data are necessary to developing a higher level of knowledge base and insights that results in better decision making, forecasting, and reliable management of sensors. This paper is a comprehensive review of the application of different machine learning algorithms in sensor data analytics within the agricultural ecosystem. It further discusses a case study on an IoT based data-driven smart farm prototype as an integrated food, energy, and water (FEW) system

Risk-based regulation of unmanned aircraft systems

The aviation sector is faced with a novel array of new airspace users including Urban Air Mobility (UAM) concepts, personal air mobility vehicles, reusable space launch vehicles, and Unmanned Aircraft Systems (UAS). Focusing on UAS, there is much effort being directed towards the development of safety regulations for this industry. National Aviation Authorities (NAA) have advocated the adoption of a risk-based approach to the development of regulations, whereby regulations are driven by the outcomes of a systematic process to assess and manage identified safety risks. Central to a risk-based approach is the Safety Risk Management Process (SRMP). A review of relevant aviation safety policy, guidance and regulatory material found that aviation safety literature does not adequately address the uncertainty inherent to any SRMP. For example, when measuring risk, only the likelihood and severity are taken into consideration, with uncertainty generally not being mentioned. Where uncertainty is recognised, it is taken into consideration through the use of conservative worst-case assumptions. This can result in the imposition of overly stringent restrictions or worse, regulations that do not adequately mitigate safety risks. Subsequently, providing a more comprehensive treatment of uncertainty in the aviation SRMP is essential to the uptake of a risk-based approach to rule-making. Further, it follows that if assessments of performance can be uncertain, then these uncertainties also need to be accounted for in other NAA regulatory processes such as the regulatory compliance assessment and compliance finding processes. It was found that the current aviation compliance process does not provide an objective means for accounting for uncertainty. As a consequence, compliance assessments can be subjective and inconsistent, with regulators lacking the tools and processes to be able to make objective compliance findings on the basis of compliance risk. A means to enable NAA to account for uncertainty in regulatory compliance processes is needed. The overall aim of this thesis is to improve regulatory outcomes under the new paradigm of risk-based regulation, through providing a conceptual framework for the rational, transparent and systematic treatment of uncertainty in the risk assessment and regulatory decision-making processes. The thesis proposes the application of Bayesian methods and normative decision theory to the aviation safety regulatory process. System Safety Regulations (SSR), commonly referred to as "Part 1309" regulations, for UAS are used as a case study. It is posited that the general theoretical approach proposed in this thesis can improve the objectivity, consistency, and transparency of current aviation regulatory processes. The generalised approaches presented in this thesis enable the adoption of risk-based rulemaking for new aviation sectors and provides the theoretical basis for risk-based compliance; a paradigm shift in how aviation safety regulators approach risk-based regulation

Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey

The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasise the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.Comment: 36 pages, 304 references, 19 Figure

Ship recognition on the sea surface using aerial images taken by Uav : a deep learning approach

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesOceans are very important for mankind, because they are a very important source of food, they have a very large impact on the global environmental equilibrium, and it is over the oceans that most of the world commerce is done. Thus, maritime surveillance and monitoring, in particular identifying the ships used, is of great importance to oversee activities like fishing, marine transportation, navigation in general, illegal border encroachment, and search and rescue operations. In this thesis, we used images obtained with Unmanned Aerial Vehicles (UAVs) over the Atlantic Ocean to identify what type of ship (if any) is present in a given location. Images generated from UAV cameras suffer from camera motion, scale variability, variability in the sea surface and sun glares. Extracting information from these images is challenging and is mostly done by human operators, but advances in computer vision technology and development of deep learning techniques in recent years have made it possible to do so automatically. We used four of the state-of-art pretrained deep learning network models, namely VGG16, Xception, ResNet and InceptionResNet trained on ImageNet dataset, modified their original structure using transfer learning based fine tuning techniques and then trained them on our dataset to create new models. We managed to achieve very high accuracy (99.6 to 99.9% correct classifications) when classifying the ships that appear on the images of our dataset. With such a high success rate (albeit at the cost of high computing power), we can proceed to implement these algorithms on maritime patrol UAVs, and thus improve Maritime Situational Awareness