Real-time system identification using deep learning for linear processes with application to unmanned aerial vehicles

This paper proposes a novel parametric identification approach for linear systems using Deep Learning (DL) and the Modified Relay Feedback Test (MRFT). The proposed methodology utilizes MRFT to reveal distinguishing frequencies about an unknown process; which are then passed to a trained DL model to identify the underlying process parameters. The presented approach guarantees stability and performance in the identification and control phases respectively, and requires few seconds of observation data to infer the dynamic system parameters. Quadrotor Unmanned Aerial Vehicle (UAV) attitude and altitude dynamics were used in simulation and experimentation to verify the presented methodology. Results show the effectiveness and real-time capabilities of the proposed approach, which outperforms the conventional Prediction Error Method in terms of accuracy, robustness to biases, computational efficiency and data requirements.Comment: 13 pages, 9 figures. Submitted to IEEE access. A supplementary video for the work presented in this paper can be accessed at: https://www.youtube.com/watch?v=dz3WTFU7W7c. This version includes minor style edits for appendix and reference

Drones Detection Using Smart Sensors

Drones are modern and sophisticated technology that have been used in numerous fields. Nowadays, many countries use them in exploration, reconnaissance operations, and espionage in military operations. Drones also have many uses that are not limited to only daily life. For example, drones are used for home delivery, safety monitoring, and others. However, the use of drones is a double-edged sword. Drones can be used for positive purposes to improve the quality of human lives, but they can also be used for criminal purposes and other detrimental purposes. In fact, many countries have been attacked by terrorists using smart drones. Hence, drone detection is an active area of research and it receives the attention of many scholars. Advanced drones are, many times, difficult to detect, and hence they, sometimes, can be life threatening. Currently, most detection methods are based on video, sound, radar, temperature, radio frequency (RF), or Wi-Fi techniques. However, each detection method has several flaws that make them imperfect choices for drone detection in sensitive areas. Our aim is to overcome the challenges that most existing drone detection techniques face. In this thesis, we propose two modeling techniques and compare them to produce an efficient system for drone detection. Specifically, we compare the two proposed models by investigating the risk assessments and the probability of success for each model

A brief review of neural networks based learning and control and their applications for robots

As an imitation of the biological nervous systems, neural networks (NN), which are characterized with powerful learning ability, have been employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification and patterns recognition etc. This article aims to bring a brief review of the state-of-art NN for the complex nonlinear systems. Recent progresses of NNs in both theoretical developments and practical applications are investigated and surveyed. Specifically, NN based robot learning and control applications were further reviewed, including NN based robot manipulator control, NN based human robot interaction and NN based behavior recognition and generation

Improved terrain type classification using UAV downwash dynamic texture effect

The ability to autonomously navigate in an unknown, dynamic environment, while at the same time classifying various terrain types, are significant challenges still faced by the computer vision research community. Addressing these problems is of great interest for the development of collaborative autonomous navigation robots. For example, an Unmanned Aerial Vehicle (UAV) can be used to determine a path, while an Unmanned Surface Vehicle (USV) follows that path to reach the target destination. For the UAV to be able to determine if a path is valid or not, it must be able to identify the type of terrain it is flying over. With the help of its rotor air flow (known as downwash e↵ect), it becomes possible to extract advanced texture features, used for terrain type classification. This dissertation presents a complete analysis on the extraction of static and dynamic texture features, proposing various algorithms and analyzing their pros and cons. A UAV equipped with a single RGB camera was used to capture images and a Multilayer Neural Network was used for the automatic classification of water and non-water-type terrains by means of the downwash e↵ect created by the UAV rotors. The terrain type classification results are then merged into a georeferenced dynamic map, where it is possible to distinguish between water and non-water areas in real time. To improve the algorithms’ processing time, several sequential processes were con verted into parallel processes and executed in the UAV onboard GPU with the CUDA framework achieving speedups up to 10x. A comparison between the processing time of these two processing modes, sequential in the CPU and parallel in the GPU, is also presented in this dissertation. All the algorithms were developed using open-source libraries, and were analyzed and validated both via simulation and real environments. To evaluate the robustness of the proposed algorithms, the studied terrains were tested with and without the presence of the downwash e↵ect. It was concluded that the classifier could be improved by per forming combinations between static and dynamic features, achieving an accuracy higher than 99% in the classification of water and non-water terrain.Dotar equipamentos moveis da funcionalidade de navegação autónoma em ambientes desconhecidos e dinâmicos, ao mesmo tempo que, classificam terrenos do tipo água e não água, são desafios que se colocam atualmente a investigadores na área da visão computacional. As soluções para estes problemas são de grande interesse para a navegação autónoma e a colaboração entre robôs. Por exemplo, um veículo aéreo não tripulado (UAV) pode ser usado para determinar o caminho que um veículo terrestre não tripulado (USV) deve percorrer para alcançar o destino pretendido. Para o UAV conseguir determinar se o caminho é válido ou não, tem de ser capaz de identificar qual o tipo de terreno que está a sobrevoar. Com a ajuda do fluxo de ar gerado pelos motores (conhecido como efeito downwash), é possível extrair características de textura avançadas, que serão usadas para a classificação do tipo de terreno. Esta dissertação apresenta uma análise completa sobre extração de texturas estáticas e dinâmicas, propondo diversos algoritmos e analisando os seus prós e contras. Um UAV equipado com uma única câmera RGB foi usado para capturar as imagens. Para classi ficar automaticamente terrenos do tipo água e não água foi usada uma rede neuronal multicamada e recorreu-se ao efeito de downwash criado pelos motores do UAV. Os re sultados da classificação do tipo de terreno são depois colocados num mapa dinâmico georreferenciado, onde é possível distinguir, em tempo real, terrenos do tipo água e não água. De forma a melhorar o tempo de processamento dos algoritmos desenvolvidos, vários processos sequenciais foram convertidos em processos paralelos e executados na GPU a bordo do UAV, com a ajuda da framework CUDA, tornando o algoritmo até 10x mais rápido. Também são apresentadas nesta dissertação comparações entre o tempo de processamento destes dois modos de processamento, sequencial na CPU e paralelo na GPU. Todos os algoritmos foram desenvolvidos através de bibliotecas open-source, e foram analisados e validados, tanto através de ambientes de simulação como em ambientes reais. Para avaliar a robustez dos algoritmos propostos, os terrenos estudados foram testados com e sem a presença do efeito downwash. Concluiu-se que o classificador pode ser melhorado realizando combinações entre as características de textura estáticas e dinâmicas, alcançando uma precisão superior a 99% na classificação de terrenos do tipo água e não água

Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field

DOES: A Deep Learning-based approach to estimate roll and pitch at sea

The use of Attitude and Heading Reference Systems (AHRS) for orientation estimation is now common practice in a wide range of applications, e.g., robotics and human motion tracking, aerial vehicles and aerospace, gaming and virtual reality, indoor pedestrian navigation and maritime navigation. The integration of the high-rate measurements can provide very accurate estimates, but these can suffer from errors accumulation due to the sensors drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and techniques. As an example, camera-based solutions have drawn a large attention by the community, thanks to their low-costs and easy hardware setup; moreover, impressive results have been demonstrated in the context of Deep Learning. This work presents the preliminary results obtained by DOES, a supportive Deep Learning method specifically designed for maritime navigation, which aims at improving the roll and pitch estimations obtained by common AHRS. DOES recovers these estimations through the analysis of the frames acquired by a low-cost camera pointing the horizon at sea. The training has been performed on the novel ROPIS dataset, presented in the context of this work, acquired using the FrameWO application developed for the scope. Promising results encourage to test other network backbones and to further expand the dataset, improving the accuracy of the results and the range of applications of the method as a valid support to visual-based odometry techniques

An advanced unmanned aerial vehicle (UAV) approach via learning-based control for overhead power line monitoring: a comprehensive review

Detection and prevention of faults in overhead electric lines is critical for the reliability and availability of electricity supply. The disadvantages of conventional methods range from cumbersome installations to costly maintenance and from lack of adaptability to hazards for human operators. Thus, transmission inspections based on unmanned aerial vehicles (UAV) have been attracting the attention of researchers since their inception. This article provides a comprehensive review for the development of UAV technologies in the overhead electric power lines patrol process for monitoring and identifying faults, explores its advantages, and realizes the potential of the aforementioned method and how it can be exploited to avoid obstacles, especially when compared with the state-of-the-art mechanical methods. The review focuses on the development of advanced Learning Control strategies for higher manoeuvrability of the quadrotor. It also explores suitable recharging strategies and motor control for improved mission autonomy

An AI-in-Loop Fuzzy-Control Technique for UAV’s Stabilization and Landing

In this paper, an adaptable fuzzy control mechanism for an Unmanned Aerial Vehicle (UAV) to manipulate its mechanical actuators is provided. The mission (landing) for the UAV is defined to track (land on) an object that is detected by a deep learning object detection algorithm. The inputs of the controller are the location and speed of the UAV that have been calculated based on the location of the detected object. Two separate fuzzy controllers are proposed to control the UAV’s motor throttle and its roll and pitch over the mission and landing time. Fuzzy logic controller (FLC) is an intelligent controller that can be used to compensate for the non-linearity behaviour of the UAV by designing a specific fuzzy rule base. These rules will be utilized to adjust the control parameters during the mission and landing period in runtime. To add the effect of the ground for tuning the FLC membership function over the landing operation, a computational flow dynamic (CFD) modeling has been investigated. The proposed techniques is evaluated on MATLAB/Simulink simulation platform and real environment. Statistical analysis of the UAV location reported during stabilization and landing process, on both simulation and real platform, show that the proposed technique outperforms the similar state-of-art control techniques for both mission and landing control.</p

UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection