Coordination on Systems of Multiple UAVs

Esta tesis trata acerca de métodos para coordinar las trayectorias de un sistema de Vehículos Aéreos no Tripulados y Autónomos (en adelante UAVs). El primer conjunto de técnicas desarrolladas durante la tesis se agrupan dentro de las técnicas de planificación de trayectorias. En este caso, el objetivo es generar planes de vuelo para un conjunto de vehículos coordinadamente de forma que no se produzcan colisiones entre ellos. Además, este tipo de técnicas puede usarse para modificar el plan de vuelo de un subconjunto de UAVs en tiempo real. Entre los algoritmos desarrollados en la tesis podemos destacar la adaptación de algoritmos evolutivos como los Algoritmos Genéticos y el Particle Swarm (Enjambre de Partículas), la incorporación de nuevas formas de muestreo del espacio para la aplicación del algoritmo Optimal Rapidly Exploring Random Trees (RRT*) en sistemas multi-UAV usando técnicas de muestreo novedosas. También se ha estudiado el comportamiento de parte de estos algoritmos en situaciones variables de incertidumbre del estado del sistema. En particular, se propone el uso del Filtro de Partículas para estimar la posición relativa entre varios UAVs. Además, se estudia la aplicación de métodos reactivos para la resolución de colisiones en tiempo real. Esta tesis propone un nuevo algoritmo para la resolución de colisiones entre múltiples UAVs en presencia de obstáculos fijos llamado G-ORCA. Este algoritmo soluciona varios problemas que han surgido al aplicar el algoritmo ORCA en su variante 3D en sistemas compuestos por vehículos reales. Su seguridad se ha demostrado tanto analíticamente, como empíricamente en pruebas con sistemas reales. De hecho, durante esta tesis numerosos experimentos en sistemas multi-UAV reales compuestos hasta por 4 UAVs han sido ejecutados. En dichos experimentos, se realiza una coordinación autónoma de UAVs en las que se asegura la ejecución de trayectorias libres de colisiones garantizando por tanto la seguridad del sistema. Una característica reseñable de esta tesis es que los algoritmos desarrollados han sido probados e integrados en sistemas más complejos que son usados en aplicaciones reales. En primer lugar, se presenta un sistema para aumentar la duración del vuelo de planeadores aprovechando las corrientes ascendentes de viento generadas por el calor (térmicas). En segundo lugar, un sistema de detección y resolución de colisiones coordinado para sistemas con múltiples UAVs reactivo ha sido diseñado, desarrollado y probado experimentalmente. Este sistema ha sido integrado dentro de un sistema automático de construcción de estructuras mediante múltiples UAVs.The aim of this thesis is to propose methods to coordinately generate trajectories for a system of Autonomous Unmanned Aerial Vehicles (UAVs). The first set of proposed techniques developed in this thesis can be defined as trajectory planning techniques. In this case, the objective is to generate coordinated flight plans for a system of UAVs in such a way that no collision are produced among each pair of UAVs. Besides, these techniques can be applied online in order to modify the original flight plan whenever a potential collision is detected. Amongst the developed algorithms in this thesis we can highlight the adaptation of evolutionary algorithms such as Genetic Algorithms and Particle Swarm, and the application of Optimal Rapidly Exploring Random Trees (RRT*) algorithm into a system of several UAVs with novel sampling techniques. In addition, many of these techniques have been adapted in order to be applicable when only uncertain knowledge of the state of the system is available. In particular, the use of the Particle Filter is proposed in order to estimate the relative position between UAVs. The estimation of the position as well as the uncertainty related to this estimation are then taken into account in the conflict resolution system. All techniques proposed in this thesis have been validated by performing several simulated and real tests. For this purpose, a method for randomly generating a huge test batch is presented in chapter 3. This will allow to test the behavior of the proposed methods in a great variety of situations. During the thesis, several real experimentations with fleets composed by up to four UAVs are presented. In these experiments, the UAVs in the system are automatically coordinated in order to ensure collision-free trajectories and thus guarantee the safety of the system. The other main topic of this thesis is the application of reactive methods for real-time conflict resolution. This thesis proposes a novel algorithm for collision resolution amongst multiple UAVs in the presence of static obstacles, which has been called Generalized-Optimal Reciprocal Collision Avoidance (G-ORCA). This algorithm overcomes several issues that have been detected into the algorithm 3D-ORCA in real applications. A remarkable characteristic of this thesis is that the developed algorithms have been applied as a part of more complex systems. First, a coordinated system for flight endurance extension of gliding aircrafts by profiting the ascending wind is presented. Second, a reactive collision avoidance block has been designed, developed and tested experimentally based in the aforementioned G-ORCA algorithm. This block has been integrated into a system for assembly construction with multiple UAVs

A survey of free software for the design, analysis, modelling, and simulation of an unmanned aerial vehicle

The objective of this paper is to analyze free software for the design, analysis, modelling, and simulation of an unmanned aerial vehicle (UAV). Free software is the best choice when the reduction of production costs is necessary; nevertheless, the quality of free software may vary. This paper probably does not include all of the free software, but tries to describe or mention at least the most interesting programs. The first part of this paper summarizes the essential knowledge about UAVs, including the fundamentals of flight mechanics and aerodynamics, and the structure of a UAV system. The second section generally explains the modelling and simulation of a UAV. In the main section, more than 50 free programs for the design, analysis, modelling, and simulation of a UAV are described. Although the selection of the free software has been focused on small subsonic UAVs, the software can also be used for other categories of aircraft in some cases; e.g. for MAVs and large gliders. The applications with an historical importance are also included. Finally, the results of the analysis are evaluated and discussed—a block diagram of the free software is presented, possible connections between the programs are outlined, and future improvements of the free software are suggested. © 2015, CIMNE, Barcelona, Spain.Internal Grant Agency of Tomas Bata University in Zlin [IGA/FAI/2015/001, IGA/FAI/2014/006

LOGISTICS IN CONTESTED ENVIRONMENTS

This report examines the transport and delivery of logistics in contested environments within the context of great-power competition (GPC). Across the Department of Defense (DOD), it is believed that GPC will strain our current supply lines beyond their capacity to maintain required warfighting capability. Current DOD efforts are underway to determine an appropriate range of platforms, platform quantities, and delivery tactics to meet the projected logistics demand in future conflicts. This report explores the effectiveness of various platforms and delivery methods through analysis in developed survivability, circulation, and network optimization models. Among other factors, platforms are discriminated by their radar cross-section (RCS), noise level, speed, cargo capacity, and self-defense capability. To maximize supply delivered and minimize the cost of losses, the results of this analysis indicate preference for utilization of well-defended convoys on supply routes where bulk supply is appropriate and smaller, and widely dispersed assets on shorter, more contested routes with less demand. Sensitivity analysis on these results indicates system survivability can be improved by applying RCS and noise-reduction measures to logistics assets.Director, Warfare Integration (OPNAV N9I)Major, Israel Defence ForcesCivilian, Singapore Technologies Engineering Ltd, SingaporeCommander, Republic of Singapore NavyCommander, United States NavyCaptain, Singapore ArmyLieutenant, United States NavyLieutenant, United States NavyMajor, Republic of Singapore Air ForceCaptain, United States Marine CorpsLieutenant, United States NavyLieutenant, United States NavyLieutenant, United States NavyLieutenant, United States NavyLieutenant, United States NavyCaptain, Singapore ArmyLieutenant Junior Grade, United States NavyCaptain, Singapore ArmyLieutenant Colonel, Republic of Singapore Air ForceApproved for public release. distribution is unlimite

Design and Simulation of a Neuroevolutionary Controller for a Quadcopter Drone

The problem addressed in the present paper is the design of a controller based on an evolutionary neural network for autonomous flight in quadrotor systems. The controller's objective is to govern the quadcopter in such a way that it reaches a specific position, bearing on attitude limitations during flight and upon reaching a target. Given the complex nature of quadcopters, an appropriate neural network architecture and a training algorithm were designed to guide a quadcopter toward a target. The designed controller was implemented as a single multi-layer perceptron. On the basis of the quadcopter's current state, the developed neurocontroller produces the correct rotor speed values, optimized in terms of both attitude-limitation compliance and speed. The neural network training was completed using a custom evolutionary algorithm whose design put particular emphasis on the cost function's definition. The developed neurocontroller was tested in simulation to drive a quadcopter to autonomously follow a complex path. The obtained simulated results show that the neurocontroller manages to effortlessly follow several types of paths with adequate precision while maintaining low travel times

Thermal Aware Design Automation of the Electronic Control System for Autonomous Vehicles

The autonomous vehicle (AV) technology, due to its tremendous social and economical benefits, is transforming the entire world in the coming decades. However, significant technical challenges still need to be overcome until AVs can be safely, reliably, and massively deployed. Temperature plays a key role in the safety and reliability of an AV, not only because a vehicle is subjected to extreme operating temperatures but also because the increasing computations demand more powerful IC chips, which can lead to higher operating temperature and large thermal gradient. In particular, as the underpinning technology for AV, artificial intelligence (AI) requires substantially increased computation and memory resources, which have been growing exponentially through recent years and further exacerbated the thermal problems. High operating temperature and large thermal gradient can reduce the performance, degrade the reliability, and even cause an IC to fail catastrophically. We believe that dealing with thermal issues must be coupled closely in the design phase of the AVs’ electronic control system (ECS). To this end, first, we study how to map vehicle applications to ECS with heterogeneous architecture to satisfy peak temperature constraints and optimize latency and system-level reliability. We present a mathematical programming model to bound the peak temperature for the ECS. We also develop an approach based on the genetic algorithm to bound the peak temperature under varying execution time scenarios and optimize the system-level reliability of the ECS. We present several computationally efficient techniques for system-level mean-time-to-failure (MTTF) computation, which show several orders-of-magnitude speed-up over the state-of-the-art method. Second, we focus on studying the thermal impacts of AI techniques. Specifically, we study how the thermal impacts for the memory bit flipping can affect the prediction accuracy of a deep neural network (DNN). We develop a neuron-level analytical sensitivity estimation framework to quantify this impact and study its effectiveness with popular DNN architectures. Third, we study the problem of incorporating thermal impacts into mapping the parameters for DNN neurons to memory banks to improve prediction accuracy. Based on our developed sensitivity metric, we develop a bin-packing-based approach to map DNN neuron parameters to memory banks with different temperature profiles. We also study the problem of identifying the optimal temperature profiles for memory systems that can minimize the thermal impacts. We show that the thermal aware mapping of DNN neuron parameters on memory banks can significantly improve the prediction accuracy at a high-temperature range than the thermal ignorant for state-of-the-art DNNs

Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones

Path planning algorithms for atmospheric science applications of autonomous aircraft systems

Among current techniques, used to assist the modelling of atmospheric processes, is an approach involving the balloon or aircraft launching of radiosondes, which travel along uncontrolled trajectories dependent on wind speed. Radiosondes are launched daily from numerous worldwide locations and the data collected is integral to numerical weather prediction.This thesis proposes an unmanned air system for atmospheric research, consisting of multiple, balloon-launched, autonomous gliders. The trajectories of the gliders are optimised for the uniform sampling of a volume of airspace and the efficient mapping of a particular physical or chemical measure. To accomplish this we have developed a series of algorithms for path planning, driven by the dual objectives of uncertainty andinformation gain.Algorithms for centralised, discrete path planning, a centralised, continuous planner and finally a decentralised, real-time, asynchronous planner are presented. The continuous heuristics search a look-up table of plausible manoeuvres generated by way of an offline flight dynamics model, ensuring that the optimised trajectories are flyable. Further to this, a greedy heuristic for path growth is introduced alongside a control for search coarseness, establishing a sliding control for the level of allowed global exploration, local exploitation and computational complexity. The algorithm is also integrated with a flight dynamics model, and communications and flight systems hardware, enabling software and hardware-in-the-loop simulations. The algorithm outperforms random search in two and three dimensions. We also assess the applicability of the unmanned air system in ‘real’ environments, accounting for the presence of complicated flow fields and boundaries. A case study based on the island South Georgia is presented and indicates good algorithm performance in strong, variable winds. We also examine the impact of co-operation within this multi-agent system of decentralised, unmanned gliders, investigating the threshold for communication range, which allows for optimal search whilst reducing both the cost of individual communication devices and the computational resources associated with the processing of data received by each aircraft. Reductions in communication radius are found to have a significant, negative impact upon the resulting efficiency of the system. To somewhat recover these losses, we utilise a sorting algorithm, determining information priority between any two aircraft in range. Furthermore, negotiation between aircraft is introduced, allowing aircraft to resolve any possible conflicts between selected paths, which helps to counteractany latency in the search heuristic

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

The total assessment profile, volume 2

Appendices are presented which include discussions of interest formulas, factors in regionalization, parametric modeling of discounted benefit-sacrifice streams, engineering economic calculations, and product innovation. For Volume 1, see

Aeronautical Engineering: A continuing bibliography with indexes, supplement 104

This bibliography lists 532 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System in December 1978