Safety Considerations for Operation of Unmanned Aerial Vehicles in the National Airspace System

There is currently a broad effort underway in the United States and internationally by several organizations to craft regulations enabling the safe operation of UAVs in the NAS. Current federal regulations governing unmanned aircraft are limited in scope, and the lack of regulations is a barrier to achieving the full potential benefit of UAV operations. To inform future FAA regulations, an investigation of the safety considerations for UAV operation in the NAS was performed. Key issues relevant to operations in the NAS, including performance and operating architecture were examined, as well as current rules and regulations governing unmanned aircraft. In integrating UAV operations in the NAS, it will be important to consider the implications of different levels of vehicle control and autonomous capability and the source of traffic surveillance in the system. A system safety analysis was performed according to FAA system safety guidelines for two critical hazards in UAV operation: midair collision and ground impact. Event-based models were developed describing the likelihood of ground fatalities and midair collisions under several assumptions. From the models, a risk analysis was performed calculating the expected level of safety for each hazard without mitigation. The variation of expected level of safety was determined based on vehicle characteristics and population density for the ground impact hazard, and traffic density for midair collisions. The results of the safety analysis indicate that it may be possible to operate small UAVs with few operational and size restrictions over the majority of the United States. As UAV mass increases, mitigation measures must be utilized to further reduce both ground impact and midair collision risks to target levels from FAA guidance. It is in the public interest to achieve the full benefits of UAV operations, while still preserving safety through effective mitigation of risks with the least possible restrictions. Therefore, a framework was presented under which several potential mitigation measures were introduced and could be evaluated. It is likely that UAVs will be significant users of the future NAS, and this report provides an analytical basis for evaluating future regulatory decisions

An information theoretic approach for generating an aircraft avoidance Markov decision process

Developing a collision avoidance system that can meet safety standards required of commercial aviation is challenging. A dynamic programming approach to collision avoidance has been developed to optimize and generate logics that are robust to the complex dynamics of the national airspace. The current approach represents the aircraft avoidance problem as Markov Decision Processes and independently optimizes a horizontal and vertical maneuver avoidance logics. This is a result of the current memory requirements for each logic, simply combining the logics will result in a significantly larger representation. The "curse of dimensionality" makes it computationally inefficient and unfeasible to optimize this larger representation. However, existing and future collision avoidance systems have mostly defined the decision process by hand. In response, a simulation-based framework was built to better understand how each potential state quantifies the aircraft avoidance problem with regards to safety and operational components. The framework leverages recent advances in signals processing and database, while enabling the highest fidelity analysis of Monte Carlo aircraft encounter simulations to date. This framework enabled the calculation of how well each state of the decision process quantifies the collision risk and the associated memory requirements. Using this analysis, a collision avoidance logic that leverages both horizontal and vertical actions was built and optimized using this simulation based approach

Evaluation of remain well clear and collision avoidance for drones

One of the cornerstones that should enable inserting unmanned aircraft into the airspace is the development of Detect and Avoid (DAA) systems. DAA systems will improve the Remote Pilot (RP) situational awareness by means of electronic conspicuity devices, providing them with the necessary means to Remain Well Clear (RWC) from other traffic and, if necessary, avoid Mid-Air collisions (MAC). DAA systems will compensate for the loss of a pilot on board, which drastically reduces the capacity to keep a safe separation from traffic, making current Rules of the Air very challenging to achieve. Given the growing popularity of drone operations for commercial and recreational purposes, new standards should include them in the not-too-distant future. Since current DAA standards and algorithms (DO-365 and ED-258) are being developed targeting large, mostly military Remotely Piloted Aircraft Systems (RPAS), this project proposes a new set of detection volumes and alert thresholds for U-Space users according to an aircraft type classification. This will allow adapting the existing DAA algorithms to small drones, complying with the new European framework of services and applications for drones (U-Space). Because testing new safety nets (such as new DAA algorithms) on real aircraft would be dangerous and inadequate, radar reports and computer-based simulations allow for a risk-free and faster evaluation of safety net performances. Due to the current lack of real drone radar tracks, this project has developed a multi-rotor drone encounter generator tool (called DEG). This software is able to generate a large number of synthetic pairwise quadcopter drone conflict tracks, simulating the instant prior to a MAC. The way trajectories are generated by DEG strongly depends on the type of operation being flown (inspection/surveillance flights and logistic flights) and the aircraft type (including a DJI F450 and a faster version called DJI F450 FAST). The results of this project include a drone conflict trajectory example generated with DEG and an investigation of the performance and effectiveness of the DEG tool using a tailored existing DAA algorithm (DAIDALUS).Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructur

Real-time collision avoidance for autonomous air vehicles

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1998.Includes bibliographical references (p. 138-139).by Christopher P. Sanders.M.S

Safety considerations for operation of different classes of unmanned aerial vehicles in the National Airspace System

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2005.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 105-109).There is currently a broad effort underway in the United States and internationally by several organizations to craft regulations enabling the safe operation of UAVs in the NAS. Current federal regulations governing unmanned aircraft are limited in scope, and the lack of regulations is a barrier to achieving the full potential benefit of UAV operations. Safety is a fundamental requirement for operation in the NAS. Maintaining and enhancing safety of UAVs is both the authority and responsibility of the Federal Aviation Administration (FAA). To inform future FAA regulations, an investigation of the safety considerations for UAV operation in the NAS was performed. Key issues relevant to operations in the NAS, including performance and operating architecture were examined, as well as current rules and regulations governing unmanned aircraft. In integrating UAV operations in the NAS, it will be important to consider the implications of different levels of vehicle control and autonomous capability and the source of traffic surveillance in the system. A system safety analysis was performed according to FAA system safety guidelines for two critical hazards in UAV operation: midair collision and ground impact. Event-based models were developed describing the likelihood of ground fatalities and midair collisions under several assumptions. From the models, a risk analysis was performed calculating the expected level of safety for each hazard without mitigation. The variation of expected level of safety was determined based on vehicle characteristics and population density for the ground impact hazard, and traffic density for midair collisions.(cont.) The results of the safety analysis indicate that it may be possible to operate small UAVs with few operational and size restrictions over the majority of the United States. As UAV mass increases, mitigation measures must be utilized to further reduce both ground impact and midair collision risks to target levels from FAA guidance. It is in the public interest to achieve the full benefits of UAV operations, while still preserving safety through effective mitigation of risks with the least possible restrictions. Therefore, a framework was presented under which several potential mitigation measures were introduced and could be evaluated. It is likely that UAVs will be significant users of the future NAS, and this thesis provides an analytical basis for evaluating future regulatory decisions.by Roland E. Weibel.S.M

Detección y evasión de obstáculos usando redes neuronales híbridas convolucionales y recurrentes

[ES] Los términos "detección y evasión" hacen referencia al requerimiento esencial de un piloto para "ver y evitar" colisiones aire-aire. Para introducir UAVs en el día a día, esta funcion del piloto debe ser replicada por el UAV. En pequeños UAVs como pueden ser los destinados a la entrega de pedidos, existen ciertos aspectos limitantes en relación a tamaño, peso y potencia, por lo que sistemas cooperativos como TCAS o ADS-B no pueden ser utilizados y en su lugar otros sistemas como cámaras electro-ópticas son candidatos potenciales para obtener soluciones efectivas. En este tipo de aplicaciones, la solución debe evitar no solo otras aeronaves sino también otros obstáculos que puedan haber cerca de la superficie donde probablemente se operará la mayoría del tiempo. En este proyecto se han utilizado redes neuronales híbridas que incluyen redes neuronales convolucionales como primera etapa para clasificar objetos y redes neuronales recurrentes a continuación para deteminar la secuencia de eventos y actuar consecuentemente. Este tipo de red neuronal es muy actual y no se ha investigado en exceso hasta la fecha, por lo que el principal objetivo del proyecto es estudiar si podrían ser aplicadas en sistemas de "detección y evasión". Algoritmos de acceso libre han sido fusionados y mejorados para crear un nuevo modelo capaz de funcionar en este tipo de aplicaciones. A parte del algoritmo de detección y seguimiento, la parte correspondiente a la evasión de colisiones también fue desarrollada. Un filtro Kalman extendido se utilizó para estimar el rango relativo entre un obstáculo y el UAV. Para obtener una resolución sobre la posibilidad de conflicto, una aproximación estocástica fue considerada. Finalmente, una maniobra de evasión geométrica fue diseñada para utilizar si fuera necesario. Esta segunda parte fue evaluada mediante una simulación que también fue creada para el proyecto. Adicionalmente, un ensayo experimental se llevó a cabo para integrar las dos partes del algoritmo. Datos del ruido de la medida fueron experimentalmente obtenidos y se comprobó que las colisiones se podían evitar satisfactoriamente con dicho valor. Las principales conclusiones fueron que este nuevo tipo funciona más rápido que los métodos basados en redes neuronales más comunes, por lo que se recomiendo seguir investigando en ellas. Con la técnica diseñada, se encuentran disponibles multiples parámetros de diseño que pueden ser adaptados a diferentes circumstancias y factores. Las limitaciones principales encontradas se centran en la detección de obstáculos y en la estimación del rango relativo, por lo que se sugiere que la futura investigación se dirija en estas direcciones.[EN] A Sense and Avoid technique has been developed in this master thesis. A special method for small UAVs which use only an electro-optical camera as the sensor has been considered. This method is based on a sophisticated processing solution using hybrid Convolutional and Recurrent Neural Networks. The aim is to study the feasibility of this kind of neural networks in Sense and Avoid applications. First, the detection and tracking part of the algorithm is presented. Two models were used for this purpose: a Convolutional Neural Network called YOLO and a hybrid Convolutional and Recurrent Neural Network called Re3. After that, the collision avoidance part was designed. This consisted of the obstacle relative range estimation using an Extended Kalman Filter, the conflict probability calculation using an analytical approach and the geometric avoidance manoeuvre generation. Both parts were assessed separately by videos and simulations respectively, and then an experimental test was carried out to integrate them. Measurement noise was experimentally tested and simulations were performed again to check that collisions were avoided with the considered detection and tracking approach. Results showed that the considered approach can track objects faster than the most common computer vision methods based on neural networks. Furthermore, the conflict was successfully avoided with the proposed technique. Design parameters were allowed to adjust speed and maneuvers accordingly to the expected environment or the required level of safety. The main conclusion was that this kind of neural network could be successfully applied to Sense and Avoid systems.Vidal Navarro, D. (2018). Sense and avoid using hybrid convolutional and recurrent neural networks. Universitat Politècnica de València. http://hdl.handle.net/10251/142606TFG

Human-Comfortable Collision Free Navigation for Personal Aerial Vehicles

Semi- or fully-autonomous Personal Aerial Vehicles (PAVs) are currently studied and developed by public and private organizations as a solution for traffic congestion. While optimal collision-free navigation algorithms have been proposed for autonomous robots, trajectories and accelerations for PAVs should also take into account human comfort. In this work, we propose a reactive decentralized collision avoidance strategy that incorporates passenger physiological comfort based on the Optimal Reciprocal Collision Avoidance strategy [1]. We study in simulation the effects of increasing PAV densities on the level of comfort, on the relative flight time and on the number of collisions per flight hour and demonstrate that our strategy reduces collision risk for platforms with limited dynamic range. Finally, we validate our strategy with a swarm of 10 quadcopters flying outdoors