94 research outputs found
UAV Model-based Flight Control with Artificial Neural Networks: A Survey
Model-Based Control (MBC) techniques have dominated flight controller designs for Unmanned Aerial Vehicles (UAVs). Despite their success, MBC-based designs rely heavily on the accuracy of the mathematical model of the real plant and they suffer from the explosion of complexity problem. These two challenges may be mitigated by Artificial Neural Networks (ANNs) that have been widely studied due to their unique features and advantages in system identification and controller design. Viewed from this perspective, this survey provides a comprehensive literature review on combined MBC-ANN techniques that are suitable for UAV flight control, i.e., low-level control. The objective is to pave the way and establish a foundation for efficient controller designs with performance guarantees. A reference template is used throughout the survey as a common basis for comparative studies to fairly determine capabilities and limitations of existing research. The end-result offers supported information for advantages, disadvantages and applicability of a family of relevant controllers to UAV prototypes
Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction
Physical motion models offer interpretable predictions for the motion of
vehicles. However, some model parameters, such as those related to aero- and
hydrodynamics, are expensive to measure and are often only roughly approximated
reducing prediction accuracy. Recurrent neural networks achieve high prediction
accuracy at low cost, as they can use cheap measurements collected during
routine operation of the vehicle, but their results are hard to interpret. To
precisely predict vehicle states without expensive measurements of physical
parameters, we propose a hybrid approach combining deep learning and physical
motion models including a novel two-phase training procedure. We achieve
interpretability by restricting the output range of the deep neural network as
part of the hybrid model, which limits the uncertainty introduced by the neural
network to a known quantity. We have evaluated our approach for the use case of
ship and quadcopter motion. The results show that our hybrid model can improve
model interpretability with no decrease in accuracy compared to existing deep
learning approaches
The AeroSonicDB (YPAD-0523) Dataset for Acoustic Detection and Classification of Aircraft
The time and expense required to collect and label audio data has been a
prohibitive factor in the availability of domain specific audio datasets. As
the predictive specificity of a classifier depends on the specificity of the
labels it is trained on, it follows that finely-labelled datasets are crucial
for advances in machine learning. Aiming to stimulate progress in the field of
machine listening, this paper introduces AeroSonicDB (YPAD-0523), a dataset of
low-flying aircraft sounds for training acoustic detection and classification
systems. This paper describes the method of exploiting ADS-B radio
transmissions to passively collect and label audio samples. Provides a summary
of the collated dataset. Presents baseline results from three binary
classification models, then discusses the limitations of the current dataset
and its future potential. The dataset contains 625 aircraft recordings ranging
in event duration from 18 to 60 seconds, for a total of 8.87 hours of aircraft
audio. These 625 samples feature 301 unique aircraft, each of which are
supplied with 14 supplementary (non-acoustic) labels to describe the aircraft.
The dataset also contains 3.52 hours of ambient background audio ("silence"),
as a means to distinguish aircraft noise from other local environmental noises.
Additionally, 6 hours of urban soundscape recordings (with aircraft
annotations) are included as an ancillary method for evaluating model
performance, and to provide a testing ground for real-time applications
Object tracking using a camera gimbal mechanism
TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia de Controle e Automação.Este trabalho apresenta um sistema de detecção e rastreamento de objetos no campo de visão da câmera acoplado a um mecanismo robótica com três graus de liberdade denominada Gimbal. O processo de detecção de objetos em tempo real usa uma ferramenta de visão computacional chamada YOLO e se comunica entre periféricos com um sistema operacional robótico (ROS) em uma aeronave pilotada remotamente (RPA) usando um computador de bordo para processar os dados.
O sistema de controle é projetado usando os conceitos matemáticos de cinemática direta e inversa do Gimbal para estimar a posição do ângulo e manter o objeto centralizado na resolução da imagem. Para comparar a matemática de controle cinemático inverso, dois controladores lineares Proporcional-Integral foram ajustados para agir com base no sinal de erro da posição do pixel para cada eixo.
Para o estudo, foi utilizado um ambiente de simulação robótica no software Gazebo para testar e ajustar os controladores realizando alguns experimentos antes de utilizá-lo na vida real, reduzindo a probabilidade de falha ou danos ao hardware. O hardware utilizado para o teste é um conjunto de componentes fornecidos por uma única empresa, facilitando a conexão entre aeronave, câmera, Gimbal e computador de bordo.
Os resultados das simulações e experimentos práticos validam a teoria e permitem que a estrutura rastreie o objeto mantendo-o no campo de visão da câmera enquanto o RPA se move para inspecionar todo o equipamento.This work presents a system development for detecting and tracking objects on the camera's field of view coupled to a mechanism of three degrees of freedom called Gimbal. The computer vision technique, You Only Look Once (YOLO), detects an object on image in execution time and communicates between peripherals at Robotic Operating System (ROS) on a remotely piloted aircraft (RPA) processing the data and controling the Gimbal's joint using an on-board computer.
The control system is designed using Gimbal's forward and inverse kinematics mathematical concepts to estimate the angle position maintaining the object centered on image resolution. In order to compare control techniques, a Proportional-Integral linear controllers have been designed to act based on error signal from pixel position to each axis independently.
To refine the algorithm it was used a robotic simulation environment from Gazebo software to test and tune controllers and perform some experiments before starting the practical tests, reducing the probability of failure or damaging the hardware. The hardware used was a set of components provided from only one company, facilitating the connection between aircraft, camera, Gimbal and on-board computer.
The results of simulation and practical experiments validate the theory and allows the mechanism to track the object maintaining it on camera's field of view while the RPA is in motion to inspect the interest area
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
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
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
Integrated Applications of Geo-Information in Environmental Monitoring
This book focuses on fundamental and applied research on geo-information technology, notably optical and radar remote sensing and algorithm improvements, and their applications in environmental monitoring. This Special Issue presents ten high-quality research papers covering up-to-date research in land cover change and desertification analyses, geo-disaster risk and damage evaluation, mining area restoration assessments, the improvement and development of algorithms, and coastal environmental monitoring and object targeting. The purpose of this Special Issue is to promote exchanges, communications and share the research outcomes of scientists worldwide and to bridge the gap between scientific research and its applications for advancing and improving society
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