Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001

Autonomous vehicle guidance in unknown environments

Gaining from significant advances in their performance granted by technological evolution, Autonomous Vehicles are rapidly increasing the number of fields of possible and effective applications. From operations in hostile, dangerous environments (military use in removing unexploded projectiles, survey of nuclear power and chemical industrial plants following accidents) to repetitive 24h tasks (border surveillance), from power-multipliers helping in production to less exotic commercial application in household activities (cleaning robots as consumer electronics products), the combination of autonomy and motion offers nowadays impressive options. In fact, an autonomous vehicle can be completed by a number of sensors, actuators, devices making it able to exploit a quite large number of tasks. However, in order to successfully attain these results, the vehicle should be capable to navigate its path in different, sometimes unknown environments. This is the goal of this dissertation: to analyze and - mainly - to propose a suitable solution for the guidance of autonomous vehicles. The frame in which this research takes its steps is the activity carried on at the Guidance and Navigation Lab of Sapienza – Università di Roma, hosted at the School of Aerospace Engineering. Indeed, the solution proposed has an intrinsic, while not limiting, bias towards possible space applications, that will become obvious in some of the following content. A second bias dictated by the Guidance and Navigation Lab activities is represented by the choice of a sample platform. In fact, it would be difficult to perform a meaningful study keeping it a very general level, independent on the characteristics of the targeted kind of vehicle: it is easy to see from the rough list of applications cited above that these characteristics are extremely varied. The Lab hosted – even before the beginning of this thesis activity – a simple, home-designed and manufactured model of a small, yet performing enough autonomous vehicle, called RAGNO (standing for Rover for Autonomous Guidance Navigation and Observation): it was an obvious choice to select that rover as the reference platform to identify solutions for guidance, and to use it, cooperating to its improvement, for the test activities which should be considered as mandatory in this kind of thesis work to validate the suggested approaches. The draft of the thesis includes four main chapters, plus introduction, final remarks and future perspectives, and the list of references. The first chapter (“Autonomous Guidance Exploiting Stereoscopic Vision”) investigates in detail the technique which has been deemed as the most interesting for small vehicles. The current availability of low cost, high performance cameras suggests the adoption of the stereoscopic vision as a quite effective technique, also capable to making available to remote crew a view of the scenario quite similar to the one humans would have. Several advanced image analysis techniques have been investigated for the extraction of the features from left- and right-eye images, with SURF and BRISK algorithm being selected as the most promising one. In short, SURF is a blob detector with an associated descriptor of 64 elements, where the generic feature is extracted by applying sequential box filters to the surrounding area. The features are then localized in the point of the image where the determinant of the Hessian matrix H(x,y) is maximum. The descriptor vector is than determined by calculating the Haar wavelet response in a sampling pattern centered in the feature. BRISK is instead a corner detector with an associated binary descriptor of 512 bit. The generic feature is identified as the brightest point in a sampling circular area of N pixels while the descriptor vector is calculated by computing the brightness gradient of each of the N(N-1)/2 pairs of sampling points. Once left and right features have been extracted, their descriptors are compared in order to determine the corresponding pairs. The matching criterion consists in seeking for the two descriptors for which their relative distance (Euclidean norm for SURF, Hamming distance for BRISK) is minimum. The matching process is computationally expensive: to reduce the required time the thesis successfully explored the theory of the epipolar geometry, based on the geometric constraint existing between the left and right projection of the scene point P, and indeed limiting the space to be searched. Overall, the selected techniques require between 200 and 300 ms on a 2.4GHz clock CPU for the feature extraction and matching in a single (left+right) capture, making it a feasible solution for slow motion vehicles. Once matching phase has been finalized, a disparity map can be prepared highlighting the position of the identified objects, and by means of a triangulation (the baseline between the two cameras is known, the size of the targeted object is measured in pixels in both images) the position and distance of the obstacles can be obtained. The second chapter (“A Vehicle Prototype and its Guidance System”) is devoted to the implementation of the stereoscopic vision onboard a small test vehicle, which is the previously cited RAGNO rover. Indeed, a description of the vehicle – the chassis, the propulsion system with four electric motors empowering the wheels, the good roadside performance attainable, the commanding options – either fully autonomous, partly autonomous with remote monitoring, or fully remotely controlled via TCP/IP on mobile networks - is included first, with a focus on different sensors that, depending on the scenario, can integrate the stereoscopic vision system. The intelligence-side of guidance subsystem, exploiting the navigation information provided by the camera, is then detailed. Two guidance techniques have been studied and implemented to identify the optimal trajectory in a field with scattered obstacles: the artificial potential guidance, based on the Lyapunov approach, and the A-star algorithm, looking for the minimum of a cost function built on graphs joining the cells of a mesh over-imposed to the scenario. Performance of the two techniques are assessed for two specific test-cases, and the possibility of unstable behavior of the artificial potential guidance, bouncing among local minima, has been highlighted. Overall, A-star guidance is the suggested solution in terms of time, cost and reliability. Notice that, withstanding the noise affecting information from sensors, an estimation process based on Kalman filtering has been also included in the process to improve the smoothness of the targeted trajectory. The third chapter (“Examples of Possible Missions and Applications”) reports two experimental campaigns adopting RAGNO for the detection of dangerous gases. In the first one, the rover accommodates a specific sensor, and autonomously moves in open fields, avoiding possible obstacles, to exploit measurements at given time intervals. The same configuration for RAGNO is also used in the second campaign: this time, however, the path of the rover is autonomously computed on the basis of the way points communicated by a drone which is flying above the area of measurements and identifies possible targets of interest. The fourth chapter (“Guidance of Fleet of Autonomous Vehicles ”) stresses this successful idea of fleet of vehicles, and numerically investigates by algorithms purposely written in Matlab the performance of a simple swarm of two rovers exploring an unknown scenario, pretending – as an example - to represent a case of planetary surface exploration. The awareness of the surrounding environment is dictated by the characteristics of the sensors accommodated onboard, which have been assumed on the basis of the experience gained with the material of previous chapter. Moreover, the communication issues that would likely affect real world cases are included in the scheme by the possibility to model the comm link, and by running the simulation in a multi-task configuration where the two rovers are assigned to two different computer processes, each of them having a different TCP/IP address with a behavior actually depending on the flow of information received form the other explorer. Even if at a simulation-level only, it is deemed that such a final step collects different aspects investigated during the PhD period, with feasible sensors’ characteristics (obviously focusing on stereoscopic vision), guidance technique, coordination among autonomous agents and possible interesting application cases

Survey of computer vision algorithms and applications for unmanned aerial vehicles

This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria.This research is supported by the Spanish Government through the CICYT projects (TRA2015-63708-R and TRA2013-48314-C3-1-R)

Vision-Based navigation system for unmanned aerial vehicles

Mención Internacional en el título de doctorThe main objective of this dissertation is to provide Unmanned Aerial Vehicles (UAVs) with a robust navigation system; in order to allow the UAVs to perform complex tasks autonomously and in real-time. The proposed algorithms deal with solving the navigation problem for outdoor as well as indoor environments, mainly based on visual information that is captured by monocular cameras. In addition, this dissertation presents the advantages of using the visual sensors as the main source of data, or complementing other sensors in providing useful information; in order to improve the accuracy and the robustness of the sensing purposes. The dissertation mainly covers several research topics based on computer vision techniques: (I) Pose Estimation, to provide a solution for estimating the 6D pose of the UAV. This algorithm is based on the combination of SIFT detector and FREAK descriptor; which maintains the performance of the feature points matching and decreases the computational time. Thereafter, the pose estimation problem is solved based on the decomposition of the world-to-frame and frame-to-frame homographies. (II) Obstacle Detection and Collision Avoidance, in which, the UAV is able to sense and detect the frontal obstacles that are situated in its path. The detection algorithm mimics the human behaviors for detecting the approaching obstacles; by analyzing the size changes of the detected feature points, combined with the expansion ratios of the convex hull constructed around the detected feature points from consecutive frames. Then, by comparing the area ratio of the obstacle and the position of the UAV, the method decides if the detected obstacle may cause a collision. Finally, the algorithm extracts the collision-free zones around the obstacle, and combining with the tracked waypoints, the UAV performs the avoidance maneuver. (III) Navigation Guidance, which generates the waypoints to determine the flight path based on environment and the situated obstacles. Then provide a strategy to follow the path segments and in an efficient way and perform the flight maneuver smoothly. (IV) Visual Servoing, to offer different control solutions (Fuzzy Logic Control (FLC) and PID), based on the obtained visual information; in order to achieve the flight stability as well as to perform the correct maneuver; to avoid the possible collisions and track the waypoints. All the proposed algorithms have been verified with real flights in both indoor and outdoor environments, taking into consideration the visual conditions; such as illumination and textures. The obtained results have been validated against other systems; such as VICON motion capture system, DGPS in the case of pose estimate algorithm. In addition, the proposed algorithms have been compared with several previous works in the state of the art, and are results proves the improvement in the accuracy and the robustness of the proposed algorithms. Finally, this dissertation concludes that the visual sensors have the advantages of lightweight and low consumption and provide reliable information, which is considered as a powerful tool in the navigation systems to increase the autonomy of the UAVs for real-world applications.El objetivo principal de esta tesis es proporcionar Vehiculos Aereos no Tripulados (UAVs) con un sistema de navegacion robusto, para permitir a los UAVs realizar tareas complejas de forma autonoma y en tiempo real. Los algoritmos propuestos tratan de resolver problemas de la navegacion tanto en ambientes interiores como al aire libre basandose principalmente en la informacion visual captada por las camaras monoculares. Ademas, esta tesis doctoral presenta la ventaja de usar sensores visuales bien como fuente principal de datos o complementando a otros sensores en el suministro de informacion util, con el fin de mejorar la precision y la robustez de los procesos de deteccion. La tesis cubre, principalmente, varios temas de investigacion basados en tecnicas de vision por computador: (I) Estimacion de la Posicion y la Orientacion (Pose), para proporcionar una solucion a la estimacion de la posicion y orientacion en 6D del UAV. Este algoritmo se basa en la combinacion del detector SIFT y el descriptor FREAK, que mantiene el desempeno del a funcion de puntos de coincidencia y disminuye el tiempo computacional. De esta manera, se soluciona el problema de la estimacion de la posicion basandose en la descomposicion de las homografias mundo a imagen e imagen a imagen. (II) Deteccion obstaculos y elusion colisiones, donde el UAV es capaz de percibir y detectar los obstaculos frontales que se encuentran en su camino. El algoritmo de deteccion imita comportamientos humanos para detectar los obstaculos que se acercan, mediante el analisis de la magnitud del cambio de los puntos caracteristicos detectados de referencia, combinado con los ratios de expansion de los contornos convexos construidos alrededor de los puntos caracteristicos detectados en frames consecutivos. A continuacion, comparando la proporcion del area del obstaculo y la posicion del UAV, el metodo decide si el obstaculo detectado puede provocar una colision. Por ultimo, el algoritmo extrae las zonas libres de colision alrededor del obstaculo y combinandolo con los puntos de referencia, elUAV realiza la maniobra de evasion. (III) Guiado de navegacion, que genera los puntos de referencia para determinar la trayectoria de vuelo basada en el entorno y en los obstaculos detectados que encuentra. Proporciona una estrategia para seguir los segmentos del trazado de una manera eficiente y realizar la maniobra de vuelo con suavidad. (IV) Guiado por Vision, para ofrecer soluciones de control diferentes (Control de Logica Fuzzy (FLC) y PID), basados en la informacion visual obtenida con el fin de lograr la estabilidad de vuelo, asi como realizar la maniobra correcta para evitar posibles colisiones y seguir los puntos de referencia. Todos los algoritmos propuestos han sido verificados con vuelos reales en ambientes exteriores e interiores, tomando en consideracion condiciones visuales como la iluminacion y las texturas. Los resultados obtenidos han sido validados con otros sistemas: como el sistema de captura de movimiento VICON y DGPS en el caso del algoritmo de estimacion de la posicion y orientacion. Ademas, los algoritmos propuestos han sido comparados con trabajos anteriores recogidos en el estado del arte con resultados que demuestran una mejora de la precision y la robustez de los algoritmos propuestos. Esta tesis doctoral concluye que los sensores visuales tienen las ventajes de tener un peso ligero y un bajo consumo y, proporcionar informacion fiable, lo cual lo hace una poderosa herramienta en los sistemas de navegacion para aumentar la autonomia de los UAVs en aplicaciones del mundo real.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Carlo Regazzoni.- Secretario: Fernando García Fernández.- Vocal: Pascual Campoy Cerver

Collaborative autonomy in heterogeneous multi-robot systems

As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition. This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems. Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots

A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives

Efficient localization plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would contribute to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities for enhancing localization of UAVs and UGVs. In this paper, we review the radio frequency (RF) based approaches for localization. We review the RF features that can be utilized for localization and investigate the current methods suitable for Unmanned vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localization for both UAVs and UGVs is examined, and the envisioned 5G NR for localization enhancement, and the future research direction are explored

Autonomous landing of fixed-wing aircraft on mobile platforms

E n esta tesis se propone un nuevo sistema que permite la operación de aeronaves autónomas sin tren de aterrizaje. El trabajo está motivado por el interés industrial en aeronaves con la capacidad de volar a gran altitud, con más capacidad de carga útil y capaces de aterrizar con viento cruzado. El enfoque seguido en este trabajo consiste en eliminar el sistema de aterrizaje de una aeronave de ala fija empleando una plataforma móvil de aterrizaje en tierra. La aeronave y la plataforma deben sincronizar su movimiento antes del aterrizaje, lo que se logra mediante la estimación del estado relativo entre ambas y el control cooperativo del movimiento. El objetivo principal de esta Tesis es el desarrollo de una solución práctica para el aterrizaje autónomo de una aeronave de ala fija en una plataforma móvil. En la tesis se combinan nuevos métodos con experimentos prácticos para los cuales se ha desarrollado un sistema de pruebas específico. Se desarrollan dos variantes diferentes del sistema de aterrizaje. El primero presta atención especial a la seguridad, es robusto ante retrasos en la comunicación entre vehículos y cumple procedimientos habituales de aterrizaje, al tiempo que reduce la complejidad del sistema. En el segundo se utilizan trayectorias optimizadas del vehículo y sincronización bilateral de posición para maximizar el rendimiento del aterrizaje en términos de requerimientos de longitud necesaria de pista, pero la estabilidad es dependiente del retraso de tiempo, con lo cual es necesario desarrollar un controlador estabilizador ampliado, basado en pasividad, que permite resolver este problema. Ambas estrategias imponen requisitos funcionales a los controladores de cada uno de los vehículos, lo que implica la capacidad de controlar el movimiento longitudinal sin afectar el control lateral o vertical, y viceversa. El control de vuelo basado en energía se utiliza para proporcionar dicha funcionalidad a la aeronave. Los sistemas de aterrizaje desarrollados se han analizado en simulación estableciéndose los límites de rendimiento mediante múltiples repeticiones aleatorias. Se llegó a la conclusión de que el controlador basado en seguridad proporciona un rendimiento de aterrizaje satisfactorio al tiempo que suministra una mayor seguridad operativa y un menor esfuerzo de implementación y certificación. El controlador basado en el rendimiento es prometedor para aplicaciones con una longitud de pista limitada. Se descubrió que los beneficios del controlador basado en el rendimiento son menos pronunciados para una dinámica de vehículos terrestres más lenta. Teniendo en cuenta la dinámica lenta de la configuración del demostrador, se eligió el enfoque basado en la seguridad para los primeros experimentos de aterrizaje. El sistema de aterrizaje se validó en diversas pruebas de aterrizaje exitosas, que, a juicio del autor, son las primeras en el mundo realizadas con aeronaves reales. En última instancia, el concepto propuesto ofrece importantes beneficios y constituye una estrategia prometedora para futuras soluciones de aterrizaje de aeronaves.In this thesis a new landing system is proposed, which allows for the operation of autonomous aircraft without landing gear. The work was motivated by the industrial need for more capable high altitude aircraft systems, which typically suffer from low payload capacity and high crosswind landing sensitivity. The approach followed in this work consists in removing the landing gear system from the aircraft and introducing a mobile ground-based landing platform. The vehicles must synchronize their motion prior to landing, which is achieved through relative state estimation and cooperative motion control. The development of a practical solution for the autonomous landing of an aircraft on a moving platform thus constitutes the main goal of this thesis. Therefore, theoretical investigations are combined with real experiments for which a special setup is developed and implemented. Two different landing system variants are developed — the safety-based landing system is robust to inter-vehicle communication delays and adheres to established landing procedures, while reducing system complexity. The performance-based landing system uses optimized vehicle trajectories and bilateral position synchronization to maximize landing performance in terms of used runway, but suffers from time delay-dependent stability. An extended passivity-based stabilizing controller was implemented to cope with this issue. Both strategies impose functional requirements on the individual vehicle controllers, which imply independent controllability of the translational degrees of freedom. Energy-based flight control is utilized to provide such functionality for the aircraft. The developed landing systems are analyzed in simulation and performance bounds are determined by means of repeated random sampling. The safety-based controller was found to provide satisfactory landing performance while providing higher operational safety, and lower implementation and certification effort. The performance-based controller is promising for applications with limited runway length. The performance benefits were found to be less pronounced for slower ground vehicle dynamics. Given the slow dynamics of the demonstrator setup, the safety-based approach was chosen for first landing experiments. The landing system was validated in a number of successful landing trials, which to the author’s best knowledge was the first time such technology was demonstrated on the given scale, worldwide. Ultimately, the proposed concept offers decisive benefits and constitutes a promising strategy for future aircraft landing solutions

Nonlinear model predictive control optimization for autonomous mobile robots

In the Agent-Target Coordination field, one of the most researched area is the coordination of a group of heterogeneous mobile agents in order to accomplish advanced tasks. Thanks to the accessibility and the improvement of Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) over the last decades, their use in exploration, research and industrial cooperative applications is increasing. However, solving challenging tasks, such as {search\&rescue} and environmental monitoring, demands the application of control laws that require high performance computational systems. Despite the components miniaturization, the complexity of developing light-weight but performing processors has lead the growth of cloud-computing. In this thesis, it is addressed the problem of driving a UGV to follow an UAV in order to set up a landing scenario, while dealing with computational resources allocation. Specifically, the target trajectory is not know in advance by the agent and the only source of information concerning the poses and velocities of both vehicles comes from the camera attached to the external computational node. To solve this problem, it is proposed a cascade of control techniques based on the Model Predictive Control (MPC) and Gaussian Process Regression (GPR) approaches. The Model Predictive Control controller is devoted to solving the Agent-Target Coordination problem by driving the UGV under the aerial vehicle. The GPR module, instead, is dedicated to predicting the computational effort of the controller, to providing the MPC control invariant $N$ and to allocating the computation of the Model Predictive Control solution locally or on the external node. Simulation results on Matlab are presented in order to illustrate and validate the proposed approach.In the Agent-Target Coordination field, one of the most researched area is the coordination of a group of heterogeneous mobile agents in order to accomplish advanced tasks. Thanks to the accessibility and the improvement of Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) over the last decades, their use in exploration, research and industrial cooperative applications is increasing. However, solving challenging tasks, such as {search\&rescue} and environmental monitoring, demands the application of control laws that require high performance computational systems. Despite the components miniaturization, the complexity of developing light-weight but performing processors has lead the growth of cloud-computing. In this thesis, it is addressed the problem of driving a UGV to follow an UAV in order to set up a landing scenario, while dealing with computational resources allocation. Specifically, the target trajectory is not know in advance by the agent and the only source of information concerning the poses and velocities of both vehicles comes from the camera attached to the external computational node. To solve this problem, it is proposed a cascade of control techniques based on the Model Predictive Control (MPC) and Gaussian Process Regression (GPR) approaches. The Model Predictive Control controller is devoted to solving the Agent-Target Coordination problem by driving the UGV under the aerial vehicle. The GPR module, instead, is dedicated to predicting the computational effort of the controller, to providing the MPC control invariant $N$ and to allocating the computation of the Model Predictive Control solution locally or on the external node. Simulation results on Matlab are presented in order to illustrate and validate the proposed approach

Situational awareness-based energy management for unmanned electric surveillance platforms

In the present day fossil fuel availability, cost, security and the pollutant emissions resulting from its use have driven industry into looking for alternative ways of powering vehicles. The aim of this research is to synthesize/design and develop a framework of novel control architectures which can result in complex powered vehicle subsystems to perform better with reduced exogeneuous information. This research looks into the area of energy management by proposing an intelligent based system which not only looks at the beaten path of where energy comes from and how much of it to use, but it goes further by taking into consideration the world around it. By operating without GPS, it realies on data such as usage, average consumption, system loads and even other surrounding vehicles are considered when making the difficult decisions of where to direct the energy into, how much of it, and even when to cut systems off in benefit of others. All this is achieved in an integrated way by working within the limitations of non-fossil fuelled energy sources like fuel cells, ultracapacitors and battery banks using driver-provided information or by crafting an artificial usage profile from historicaly learnt data. By using an organic computing philosophy based on artificial intelligence this alternative approach to energy supply systems presents a different perspective beginning by accepting the fact that when hardware is set energy can be optimized only so much and takes a step further by answering the question of how to best manage it when refuelling might not be an option. The result is a situationally aware system concept that is portable to any type of electrically powered platform be it ground, aerial or marine since it operates on the fact that all operate within three dimensional space. The system´s capabilities are then verified in a virtual reality environment which can be tailored to the meet reseach needs including allowing for different altitudes, environmental temperature and humidity profiles. This VR system is coupled with a chassis dynamometer to allow for testing of real physical prototype unmanned ground vehicles where the intelligent system will benefit by learning from real platform data. The Thesis contributions and objectives are summarised next: The control system proposed includes an awareness of the surroundings within which the vehicle is operating without relying on GPS position information. The system proposed is portable and could be used to control other systems. The test platform developed within the Thesis is flexible and could be used for other systems. The control system for the fuel cell system described within the work has included an allowance for altitude and humidity. These factors would appear to be significant for such systems. The structure of the control system and its hierarchy is novel. The ability of the system to be applied to a UAV and as such control a ‘vehicle’ in 3 dimensions, and yet be also applied to a ground vehicle, where roll and pitch are largely a function of the ground over which it travels (so the UGV only uses a subset of the control functionality). The mission awareness of the control structure appears to be the heart of the potential contribution to knowledge, and that this also includes the ability to create an estimated, artificial mission profile should one not be input by the operators. This learnt / adaptive input could be expanded on to highlight this aspect

Automation and Control

Advances in automation and control today cover many areas of technology where human input is minimized. This book discusses numerous types and applications of automation and control. Chapters address topics such as building information modeling (BIM)–based automated code compliance checking (ACCC), control algorithms useful for military operations and video games, rescue competitions using unmanned aerial-ground robots, and stochastic control systems