scholarly journals A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Adrian Carrio ◽  
Carlos Sampedro ◽  
Alejandro Rodriguez-Ramos ◽  
Pascual Campoy
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Complex Data ◽  
Aerial Vehicles ◽  
Detailed Explanation ◽  
Learning Techniques ◽  
Security Surveillance ◽  
And Control ◽  
Robotic Tasks ◽  
Robotic Applications

Deep learning is recently showing outstanding results for solving a wide variety of robotic tasks in the areas of perception, planning, localization, and control. Its excellent capabilities for learning representations from the complex data acquired in real environments make it extremely suitable for many kinds of autonomous robotic applications. In parallel, Unmanned Aerial Vehicles (UAVs) are currently being extensively applied for several types of civilian tasks in applications going from security, surveillance, and disaster rescue to parcel delivery or warehouse management. In this paper, a thorough review has been performed on recent reported uses and applications of deep learning for UAVs, including the most relevant developments as well as their performances and limitations. In addition, a detailed explanation of the main deep learning techniques is provided. We conclude with a description of the main challenges for the application of deep learning for UAV-based solutions.

scholarly journals Urban Natural Environment Analysis through Deep Learning Techniques on Automatically Acquired Images from Unmanned Aerial Vehicles

2020 ◽  
Author(s):  
Marco A Moreno-Armendáriz ◽  
Hiram Calvo ◽  
Carlos A Duchanoy ◽  
Anayantzin P López-Juárez ◽  
Israel A Vargas-Monroy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Natural Environment ◽  
Environment Analysis ◽  
Aerial Vehicles ◽  
Learning Techniques

scholarly journals Evaluation of Reinforcement and Deep Learning Algorithms in Controlling Unmanned Aerial Vehicles

2021 ◽  
Vol 11 (16) ◽  
pp. 7240
Author(s):  
Yalew Zelalem Jembre ◽  
Yuniarto Wimbo Nugroho ◽  
Muhammad Toaha Raza Khan ◽  
Muhammad Attique ◽  
Rajib Paul ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Physical Environment ◽  
Learning Algorithms ◽  
Q Learning ◽  
Planning And Control ◽  
Aerial Vehicles ◽  
Reinforcement Learning Models ◽  
And Control

Unmanned Aerial Vehicles (UAVs) are abundantly becoming a part of society, which is a trend that is expected to grow even further. The quadrotor is one of the drone technologies that is applicable in many sectors and in both military and civilian activities, with some applications requiring autonomous flight. However, stability, path planning, and control remain significant challenges in autonomous quadrotor flights. Traditional control algorithms, such as proportional-integral-derivative (PID), have deficiencies, especially in tuning. Recently, machine learning has received great attention in flying UAVs to desired positions autonomously. In this work, we configure the quadrotor to fly autonomously by using agents (the machine learning schemes being used to fly the quadrotor autonomously) to learn about the virtual physical environment. The quadrotor will fly from an initial to a desired position. When the agent brings the quadrotor closer to the desired position, it is rewarded; otherwise, it is punished. Two reinforcement learning models, Q-learning and SARSA, and a deep learning deep Q-network network are used as agents. The simulation is conducted by integrating the robot operating system (ROS) and Gazebo, which allowed for the implementation of the learning algorithms and the physical environment, respectively. The result has shown that the Deep Q-network network with Adadelta optimizer is the best setting to fly the quadrotor from the initial to desired position.

scholarly journals A new nonlinear lifting line method for aerodynamic analysis and deep learning modeling of small unmanned aerial vehicles

2021 ◽  
Vol 13 ◽  
pp. 175682932110168
Author(s):  
Hasan Karali ◽  
Gokhan Inalhan ◽  
M Umut Demirezen ◽  
M Adil Yukselen
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
Computationally Intensive ◽  
Lifting Line ◽  
Lifting Line Method ◽  
Nonlinear Aerodynamic

In this work, a computationally efficient and high-precision nonlinear aerodynamic configuration analysis method is presented for both design optimization and mathematical modeling of small unmanned aerial vehicles. First, we have developed a novel nonlinear lifting line method which (a) provides very good match for the pre- and post-stall aerodynamic behavior in comparison to experiments and computationally intensive tools, (b) generates these results in order of magnitudes less time in comparison to computationally intensive methods such as computational fluid dynamics. This method is further extended to a complete configuration analysis tool that incorporates the effects of basic fuselage geometries. Moreover, a deep learning based surrogate model is developed using data generated by the new aerodynamic tool that can characterize the nonlinear aerodynamic performance of unmanned aerial vehicles. The major novel feature of this model is that it can predict the aerodynamic properties of unmanned aerial vehicle configurations by using only geometric parameters without the need for any special input data or pre-process phase as needed by other computational aerodynamic analysis tools. The obtained black-box function can calculate the performance of an unmanned aerial vehicle over a wide angle of attack range on the order of milliseconds, whereas computational fluid dynamics solutions take several days/weeks in a similar computational environment. The aerodynamic model predictions show an almost 1-1 coincidence with the numerical data even for configurations with different airfoils that are not used in model training. The developed model provides a highly capable aerodynamic solver for design optimization studies as demonstrated through an illustrative profile design example.

Modeling and control of a quadrotor tail-sitter unmanned aerial vehicles

2021 ◽  
pp. 095965182110504
Author(s):  
Hongbo Xin ◽  
Yujie Wang ◽  
Xianzhong Gao ◽  
Qingyang Chen ◽  
Bingjie Zhu ◽  
...  
Keyword(s):  
Control System ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Euler Angle ◽  
Level Flight ◽  
Aerial Vehicles ◽  
Flight Mode ◽  
Aerial Vehicle ◽  
Hover Flight ◽  
And Control

The tail-sitter unmanned aerial vehicles have the advantages of multi-rotors and fixed-wing aircrafts, such as vertical takeoff and landing, long endurance and high-speed cruise. These make the tail-sitter unmanned aerial vehicle capable for special tasks in complex environments. In this article, we present the modeling and the control system design for a quadrotor tail-sitter unmanned aerial vehicle whose main structure consists of a traditional quadrotor with four wings fixed on the four rotor arms. The key point of the control system is the transition process between hover flight mode and level flight mode. However, the normal Euler angle representation cannot tackle both of the hover and level flight modes because of the singularity when pitch angle tends to [Formula: see text]. The dual-Euler method using two Euler-angle representations in two body-fixed coordinate frames is presented to couple with this problem, which gives continuous attitude representation throughout the whole flight envelope. The control system is divided into hover and level controllers to adapt to the two different flight modes. The nonlinear dynamic inverse method is employed to realize fuselage rotation and attitude stabilization. In guidance control, the vector field method is used in level flight guidance logic, and the quadrotor guidance method is used in hover flight mode. The framework of the whole system is established by MATLAB and Simulink, and the effectiveness of the guidance and control algorithms are verified by simulation. Finally, the flight test of the prototype shows the feasibility of the whole system.

Design And Control Of Plasma Actuated Unmanned Aerial Vehicles (UAV)

2017 ◽  
Author(s):  
Magesh T. Rajan ◽  
Hao Xu ◽  
Clyde Avalos ◽  
Anthony Matheson ◽  
Eric Swinny
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Aerial Vehicles ◽  
And Control

Systematic Modeling of Rotor-Driving Dynamics for Small Unmanned Aerial Vehicles

2018 ◽  
Vol 06 (02) ◽  
pp. 81-93
Author(s):  
Limin Wu ◽  
Yijie Ke ◽  
Ben M. Chen
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Frequency Domain ◽  
Frequency Domain Analysis ◽  
Speed Controller ◽  
Aerial Vehicles ◽  
Dynamics And Control ◽  
Semi Empirical ◽  
And Control ◽  
Cifer Software ◽  
Systematic Modeling

This paper proposes a systematic modeling approach of rotor-driving dynamics for small unmanned aerial vehicles (UAVs) based on system identification and first principle-based methods. Both steady state response analyses and frequency-domain identifications are conducted for the rotor, and Comprehensive Identification from Frequency Responses (CIFER) software is mainly utilized for the frequency-domain analysis. Moreover, a novel semi-empirical model integrating the rotor and the electrical speed controller is presented and validated. The demonstrated results and model are promising in UAV dynamics and control applications.

Sensorship: the seen unseen of drone warfare

2016 ◽  
Author(s):  
Tom Holert
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Military Operations ◽  
Legal Problems ◽  
Aerial Vehicles ◽  
Drone Warfare ◽  
And Control ◽  
The Military ◽  
Blind Spots

Contemporary warfare has been significantly transformed by the promotion and implementation of unmanned aerial vehicles (or drones) into global military operations. Networked remote sensory vision and the drones’ capability to carry deadly missiles entail and facilitate increasingly individualised, racialised, and necropolitical military practices conceptualised as ‘surgical strikes’ or ‘targeted killings’, all in the name of ‘counterinsurgency’. In the absence of publicly accessible documentations of ‘drone vision’, images of drones themselves constitute what is arguably one of the most contested iconographies of the present. The ethical and legal problems engendered by the virtualisation of violence and the panoptical fantasies of persistent vision and continuous threat interfere with the commercial interests and the publicised ideas of ‘clean’ warfare of the military-industrial-media complex. Drones have become a fetishised icon of warfare running out of human measure and control and are henceforth challenged by activist strategies highlighting the blind spots and victims of their deployment.

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