Emergency Landing Spot Detection for Unmanned Aerial Vehicle

The use and research of Unmanned Aerial Vehicle (UAV) have been increasing over the years due to the applicability in several operations such as search and rescue, delivery, surveillance and others. Considering the increased presence of these vehicles in the airspace, it becomes necessary to reflect on the safety issues or failures that UAV may have and what is the appropriate action to take. Furthermore, in many missions the vehicle will not return to its original location and, in case of fail to achieve the landing spot, need to have onboard capability to estimate the best spot to safely land. The vehicles are susceptible to external disturbance or electromechanical malfunction. In this emergency’s scenarios, UAVs must safely land in a way that will minimize damage to the robot and will not cause any human injury. The suitability of a landing site depends on two main factors: the distance of the aircraft to the landing site and the ground conditions. The ground conditions are all the factors that are relevant when the aircraft is in contact with the ground, such as slope, roughness and presence of obstacles. This dissertation addresses the scenario of finding a safe landing spot during operation. Therefore, the algorithm must be able to classify the incoming data and store the location of suitable areas. Specifically, by processing Light Detection and Ranging (LiDAR) data to identify potential landing zones and evaluating the detected spots continuously given certain conditions. In this dissertation, it was developed a method that analyses geometric features on point cloud data and detects potential good spots. The algorithm uses the Principal Component Analysis (PCA) to find planes in point clouds clusters. The planes that have slope less than a threshold are considered potential landing spots. These spots are then evaluated regarding ground and vehicles conditions such as the distance to the UAV, presence of obstacles, roughness of the area, slope of the spot. The output of the algorithm is the optimum spot to land and can vary during operation.O uso e pesquisa de veículos aéreos não tripulados (VANT) têm aumentado ao longo dos anos devido à aplicabilidade em diversas operações, como busca e salvamento, entrega, vigilância e outras. Considerando a crescente presença desses veículos no espaço aéreo, torna-se necessário refletir sobre os problemas ou falhas de segurança que o veículo pode ter e qual é a ação apropriada a ser tomada. Além disso, em muitas missões, o veículo não retornará ao seu local original e, caso não seja possível alcançar a zona de aterragem, precisa ter a capacidade de estimar o melhor ponto para aterrar em segurança. Os veículos são suscetíveis a perturbações externas ou mau funcionamento eletromecânico. Nesses cenários de emergência, os UAVs precisam aterrar com segurança de forma a minimizar os danos ao robô e não causar ferimentos em pessoas. A adequação de um local de pouso depende de dois fatores principais: a distância do veículo aéreo ao local de pouso e as condições do solo. As condições do solo são todos os fatores relevantes quando a aeronave está em contacto com o solo, como declividade, rugosidade e presença de obstáculos. Esta dissertação aborda o cenário de encontrar um local de pouso seguro durante a operação. Portanto, o algoritmo deve ser capaz de classificar os dados recebidos e armazenar a localização de áreas adequadas. Especificamente, processando dados de LiDAR para identificar possíveis zonas de aterragem e avaliando os pontos detetados continuamente, dadas determinadas condições. Nesta dissertação, foi desenvolvido um método que analisa características geométricas em nuvem de pontos e deteta possíveis bons locais de aterragem. O algoritmo usa a Análise de Componente Principal (PCA) para encontrar planos em clusters de nuvens de pontos. Os planos com inclinação menor que um limite são considerados possíveis pontos de aterragem. Esses pontos são então avaliados quanto às condições do solo e dos veículos, como a distância ao UAV, presença de obstáculos, rugosidade da área, inclinação do ponto. A saída do algoritmo é o local ideal para aterrar e pode variar durante a operação

Safe2Ditch Steer-To-Clear Development and Flight Testing

This paper describes a series of small unmanned aerial system (sUAS) flights performed at NASA Langley Research Center in April and May of 2019 to test a newly added Steer-to-Clear feature for the Safe2Ditch (S2D) prototype system. S2D is an autonomous crash management system for sUAS. Its function is to detect the onset of an emergency for an autonomous vehicle, and to enable that vehicle in distress to execute safe landings to avoid injuring people on the ground or damaging property. Flight tests were conducted at the City Environment Range for Testing Autonomous Integrated Navigation (CERTAIN) range at NASA Langley. Prior testing of S2D focused on rerouting to an alternate ditch site when an occupant was detected in the primary ditch site. For Steer-to-Clear testing, S2D was limited to a single ditch site option to force engagement of the Steer-to-Clear mode. The implementation of Steer-to-Clear for the flight prototype used a simple method to divide the target ditch site into four quadrants. An RC car was driven in circles in one quadrant to simulate an occupant in that ditch site. A simple implementation of Steer-to- Clear was programmed to land in the opposite quadrant to maximize distance to the occupants quadrant. A successful mission was tallied when this occurred. Out of nineteen flights, thirteen resulted in successful missions. Data logs from the flight vehicle and the RC car indicated that unsuccessful missions were due to geolocation error between the actual location of the RC car and the derived location of it by the Vision Assisted Landing component of S2D on the flight vehicle. Video data indicated that while the Vision Assisted Landing component reliably identified the location of the ditch site occupant in the image frame, the conversion of the occupants location to earth coordinates was sometimes adversely impacted by errors in sensor data needed to perform the transformation. Logged sensor data was analyzed to attempt to identify the primary error sources and their impact on the geolocation accuracy. Three trends were observed in the data evaluation phase. In one trend, errors in geolocation were relatively large at the flight vehicles cruise altitude, but reduced as the vehicle descended. This was the expected behavior and was attributed to sensor errors of the inertial measurement unit (IMU). The second trend showed distinct sinusoidal error for the entire descent that did not always reduce with altitude. The third trend showed high scatter in the data, which did not correlate well with altitude. Possible sources of observed error and compensation techniques are discussed

Vision-based Safe Autonomous UAV Docking with Panoramic Sensors

The remarkable growth of unmanned aerial vehicles (UAVs) has also sparked concerns about safety measures during their missions. To advance towards safer autonomous aerial robots, this work presents a vision-based solution to ensuring safe autonomous UAV landings with minimal infrastructure. During docking maneuvers, UAVs pose a hazard to people in the vicinity. In this paper, we propose the use of a single omnidirectional panoramic camera pointing upwards from a landing pad to detect and estimate the position of people around the landing area. The images are processed in real-time in an embedded computer, which communicates with the onboard computer of approaching UAVs to transition between landing, hovering or emergency landing states. While landing, the ground camera also aids in finding an optimal position, which can be required in case of low-battery or when hovering is no longer possible. We use a YOLOv7-based object detection model and a XGBooxt model for localizing nearby people, and the open-source ROS and PX4 frameworks for communication, interfacing, and control of the UAV. We present both simulation and real-world indoor experimental results to show the efficiency of our methods

Autonomous Capabilities for Small Unmanned Aerial Systems Conducting Radiological Response: Findings from a High-fidelity Discovery Experiment

This article presents a preliminary work domain theory and identifies autonomous vehicle, navigational, and mission capabilities and challenges for small unmanned aerial systems (SUASs) responding to a radiological disaster. Radiological events are representative of applications that involve flying at low altitudes and close proximities to structures. To more formally understand the guidance and control demands, the environment in which the SUAS has to function, and the expected missions, tasks, and strategies to respond to an incident, a discovery experiment was performed in 2013. The experiment placed a radiological source emitting at 10 times background radiation in the simulated collapse of a multistory hospital. Two SUASs, an AirRobot 100B and a Leptron Avenger, were inserted with subject matter experts into the response, providing high operational fidelity. The SUASs were expected by the responders to fly at altitudes between 0.3 and 30 m, and hover at 1.5 m from urban structures. The proximity to a building introduced a decrease in GPS satellite coverage, challenging existing vehicle autonomy. Five new navigational capabilities were identified: scan, obstacle avoidance, contour following, environment-aware return to home, andreturn to highest reading. Furthermore, the data-to-decision process could be improved with autonomous data digestion and visualization capabilities. This article is expected to contribute to a better understanding of autonomy in a SUAS, serve as a requirement document for advanced autonomy, and illustrate how discovery experimentation serves as a design tool for autonomous vehicles

Threat Management Methodology for Unmanned Aerial Systems operating in the U-space

This paper presents a threat management methodology for Unmanned Aircraft Systems (UAS) operating in the civil airspace. The work is framed within an Unmanned Traffic Management (UTM) system based on the U-space initiative. We propose a new method that focuses on providing the required automated decision-making during real-time threat management and conflict resolution, which is one of the main gaps in the current U-space ecosystem. Our method is capable of handling all commonplace UTM threats, as well as selecting optimal mitigation actions, trading off efficiency and safety. Our implementation is open-source and fully integrated in a UTM software architecture, implementing U-space services related to emergency management and tactical deconfliction. We demonstrate our methodology through a set of realistic use cases with actual UAS operating in civil airspace. For that, we performed field experiments in an aerodrome with segregated airspace, and we showcased that the methodology is capable of autonomously managing heterogeneous threats in real time.Unión Europea - Horizonte 2020 77629

Continuous Autonomous UAV Inspection for FPSO vessels

This Master's thesis represents the preliminary design study and proposes the unmanned aerial vehicle (UAV) -based inspection framework, comprising several multirotors with automatic charging and deployment for 24/7 integrity inspection tasks. This project has three main topics. First one describes the operational environment and existing regulations that cover use of UAVs. It forms the basis for proposal of the relevant use-case scenarios. Third part comprises two chapters, where design of concept and framework is being based on the previous factors. It shows that before implementation of fully autonomous inspection system, there is a need to cover both regulatory and technical gaps. It can be explained by the fact that there does not exist any autonomous inspection system today. Thus, this project can be seen as a base for future development of the UAV-based inspection system, as it focuses on creation of a general framework

Mock Certification Basis for an Unmanned Rotorcraft for Precision Agricultural Spraying

This technical report presents the results of a case study using a hazard-based approach to develop preliminary design and performance criteria for an unmanned agricultural rotorcraft requiring airworthiness certification. This case study is one of the first in the public domain to examine design and performance criteria for an unmanned aircraft system (UAS) in tandem with its concept of operations. The case study results are intended to support development of airworthiness standards that could form a minimum safety baseline for midsize unmanned rotorcraft performing precision agricultural spraying operations under beyond visual line-of-sight conditions in a rural environment. This study investigates the applicability of current methods, processes, and standards for assuring airworthiness of conventionally piloted (manned) aircraft to assuring the airworthiness of UAS. The study started with the development of a detailed concept of operations for precision agricultural spraying with an unmanned rotorcraft (pp. 5-18). The concept of operations in conjunction with a specimen unmanned rotorcraft were used to develop an operational context and a list of relevant hazards (p. 22). Minimum design and performance requirements necessary to mitigate the hazards provide the foundation of a proposed (or mock) type certification basis. A type certification basis specifies the applicable standards an applicant must show compliance with to receive regulatory approval. A detailed analysis of the current airworthiness regulations for normal-category rotorcraft (14 Code of Federal Regulations, Part 27) was performed. Each Part 27 regulation was evaluated to determine whether it mitigated one of the relevant hazards for the specimen UAS. Those regulations that did were included in the initial core of the type certification basis (pp. 26-31) as written or with some simple modifications. Those regulations that did not mitigate a recognized hazard were excluded from the certification basis. The remaining regulations were applicable in intent, but the text could not be easily tailored. Those regulations were addressed in separate issue papers. Exploiting established regulations avoids the difficult task of generating and interpreting novel requirements, through the use of acceptable, standardized language. The rationale for the disposition of the regulations was assessed and captured (pp. 58-115). The core basis was then augmented by generating additional requirements (pp. 38-47) to mitigate hazards for an unmanned sprayer that are not covered in Part 27