Autonomous Quadrotor

İnsansız Hava Araçları üzerine yapılan çalışmalar son yıllarda popülerliği gittikçe artan akademik çalışma kaynağı ve mühendislik uygulama alanı haline gelmiştir. İHA’lar arasında üzerine yapılan çalışmaların fazlalığı açısından ve basit yapısından dolayı bu tezde quadrokopterler incelenmiştir. Quadrokopterler 4 rotora sahip döner kanatlı dikey iniş kalkış yapabilen 6 serbestlik dereceli İnsansız Hava Aracı olarak tanımlanır.
Bu çalışmada multikopter sistemleri üzerine çalışılmış, İnsansız Hava Araçları’ nın ne amaçla kullanıldığı, quadrokopterin bu sistemler içerisindeki yeri incelenmiş ve görsel öğerlerle zenginleştirilerek quadrokopter uygulaması yapılmıştır. Quadrokopterlerin kullanım amaçları ve gelecekte kullanılabilecek alanlar incelenmiştir. Multikopter sistemlerinde kullanılan malzemeler detaylı bir şekilde açıklanmış, nasıl kullanılacakları anlatılmıştır. Anlık veri iletimi üzerine çalışılmış sensör verilerinin aktarılması ve görüntü aktarım işlemlerine detaylı bir şekilde değinilmiştir.

This paper presents the modeling of a four rotor vertical take-off and landing (VTOL) unmanned air vehicle known as the quad rotor aircraft. The paper presents a new model design method for the flight control of an autonomous quad rotor. The paper describes the controller architecture for the quad rotor as well. The dynamic model of the quad-rotor, which is an under actuated aircraft with fixed four pitch angle rotors was described. The Modeling of a quad rotor vehicle is not an easy task because of its complex structure. The aim is to develop a model of the vehicle as realistic as possible. The model is used to design a stable and accurate controller. This paper explains the developments of a PID (proportional-integral-derivative) control method to obtain stability in flying the Quad-rotor flying object.The model has four input forces which are basically the thrust provided by each propeller connected to each rotor with fixed angle. Forward (backward) motion is maintained by increasing (decreasing) speed of front (rear) rotor speed while decreasing (increasing) rear (front) rotor speed simultaneously which means changing the pitch angle. Left and right motion is accomplished by changing roll angle by the same way. The front and rear motors rotate counter-clockwise while other motors rotate clockwise so that the yaw command is derived by increasing (decreasing) counter-clockwise motors speed while decreasing (increasing) clockwise motor speeds.

haritalama, gözlem, ürün taşıma ve teslimi veya hedef tespiti
ve takibi gibi çeşitli kullanım alanlarına sahip olan İHA’lar
malzeme bilimi ve üretim alanlarındaki gün geçtikçe artan
gelişmeler sayesinde daha farklı alanlarda ve daha yaygın
olarak kullanılmaktadır. Döner kanat İHA’lar dikey kalkışiniş,
bulundukları yerde havada asılı kalabilme ve agresif
manevra yetenekleriyle ulaşılması zor alanlanlardaki
görevleri rahatlıkla gerçekleştirebilmektedir. Bu görevler
esnasında İHA’nın uzaktan kumanda ile pilot tarafından
kontrol edilmesi yerine otopilot destekli uçuş moduyla yer
istasyonu başındaki pilot tarafından veya tam otonom uçuş ile
pilot olmadan kontrol edilmesi pilot hatalarının önüne
geçmekle kalmayıp pilotun İHA’nın görev yerinden bağımsız
olarak İHA’yı kontrol etmesine imkan tanımakta; aynı
zamanda herhangi bir pilota ihtiyaç duymadan ön tanımlı
görevlerin yerine getirilmesini mümkün kılmaktadır. Bu
çalışmada bir dört rotorlu hava aracı için özgün bir otopilot
sistemi geliştirilmiş, sistemin tasarımı, bileşenleri ve işleyişi
üzerinde durulmuştur.

Mathematical modeling and simulation of an
unmanned aerial vehicle, specifically, quadrotor modeling
is not an easy task because of its complex structure, nonlinear
dynamics and under-actuated nature. In this paper
a dynamic model of a quadrotor has been developed using
MATLAB/SIMULINK software platform. The aim is to
model a quadrotor vehicle as realistic as possible. The
model is then used to design a PID controller structure to
stabilize the roll, pitch and yaw angles of the quad rotor
system. The developed systems id tested successfully for
number of numerical simulation runs.

ABSTRCT The existing neural networks activation functions, the Sigmoid among others is the only set uses across various applications of NNs such as microscopy, neuromorphic, optical, robotics, finance and transportation. Only one set applies to different areas of application. Also, these activation functions' selections are based on trial and error, neither emanate from the AI and or training datasets, nor from the testing data. This formed NNs' Black-box. Jamilu (2019) proposed that strong links between the AI and or training datasets and activation functions must be established. This is to replace the NNs' Black-box models with the models rely much less on experts' assumptions, and much more on input AI and or training datasets, time change and specific area of application. Thus, Jamilu (2019) proposed Criterion(s) for the rational selection of activation functions. The paper is to use superintelligent NNs for stock price predictions, portfolio optimization, and general application approaches to shed light on the paper' title.

This work presents the modeling and control of a multi-body air vehicle composed of a miniature aerial vehicle (MAV) and mechanical device, a camera positioner, augmented aerial platform which is considered as the logical next framework within the MAV civil applications. The goal is to improve the current operational profile of visual sensors onboard the MAV, by broading current aerial configurations for visual sensors tasks with novel capabilities to disengage the dynamic coupling due to typical setups. The Euler-Lagrange formalism is applied aiming at obtaining coupling terms between the aerial and the camera positioner systems. Results of two nonlinear control techniques applied to the MAV are presented. A backstepping and a sliding-mode techniques are first designed and then numerically simulated. Finally, we discuss the results of each approach.

The  aim  of  optimal  power  flow  is  to discover  an  operating  point  that  minimizes  the  generation  cost while satisfying  multiple  operating constraints.  Over the years, several techniques have been introduced to solve this non-linear optimization  problem.  In  this  paper,  genetic  algorithm deploying  optimum  non-uniform  mutation rate and elitism has been  used  to  solve  this  problem.  After  implementation of  this algorithm  in  MATLAB,  the  data  of  IEEE  30-bus practical power  system  and  NTDC  32-bus  test  system  of Pakistan  have been  solved  for  optimal  power  flow  and  results  have  been compared  with  the  previously  used  techniques  such  as;  simple genetic  algorithm (SGA), linear programming (LP), ant colony optimization  (ACO),  differential  evolution  (DE)  and  artificial bee  colony  algorithm  (ABC).  It  has  been  established  that  the proposed  solution  proves  to  be  more  cost  effective  than previously  used  techniques.  The  proposed  technique  offers annual cost saving of  $6061630.92  for NTDC 32-bus test system.
The capital thus saved can be utilized to pay  back  circular debt
and  hence  the  problem  of  load  shedding  in  Pakistan  can  be alleviated.

Keywords— Genetic Algorithm, NTDC Economic Load Dispatch, Optimal Power Flow.

A neural network control scheme with an adaptive observer is proposed in this paper to Quadrotor helicopter stabilization. The unknown part in Quadrotor dynamical model was estimated on line by a Single Hidden Layer network. To solve the non measurable states problem a new adaptive observer was proposed. The main purpose here is to reduce the measurement noise amplification caused by conventional high gain observer by introducing some changes in observer's original structure that can minimize the variance and the amplitude of the noisy signal without increasing tracking error. The stability analysis of the overall closed-loop system/ observer is performed using the Lyapunov direct method. Simulation results are given to highlight the performances of the proposed scheme.

For most autonomous robots it is crucial to know where the robot is located and where it is heading to, so it can plan its next steps. Especially for stabilizing the attitude of an unmanned aerial vehicle (UAV), a high degree of precision and accuracy is required.
The goal of this work was to enable the UAV to hover and land safely without relying on optical sensors.
Therefore, we introduce a state estimation algorithm for a quadrotor using an inertial measurement unit (IMU), a barometer and a sonar sensor.
We have developed an Extended Kalman Filter (EKF) that uses rigid body dynamics for the prediction, while transforming the information of the sensor signals to information about the states for the correction.
We tested the algorithm using real sensor data in a specifically developed Matlab framework, where direct and indirect variations of an EKF, as well as a non-linear Unscented Kalman Filter (UKF), are compared. Furthermore, we evaluated the implementation on the PX4FMU micro-controller of the pixhawk project.
The algorithm outlined in this work produces a state estimate for the height and attitude of a rotorcraft with any number of rotors that are fixed pitch.

This paper presents information about
quadrocopter designed for special aim and its applications. In
this context, it focuses basic subjects such as design of a
quadrocopter, obtaining numerical data, detection of foreign
matter on runway. The measurements and graphs belongs to the
subject in point are included. The analyses to be carried out put
forward that the quadrocopter has many fields of use contribute
to safety of aircraft take-off and landing.

UAVTraffic (cro. Višeagentno upravljanje prometom autonomnih bespilotnih letjelica - Multi-agent based Traffic Control of Autonomous Unmanned Aerial Vehicles) was financed by the University of Zagreb from August 2014 until December 2014. The technology of unmanned aerial vehicles has matured for various civil usage scenarios and is already being tested by major industry players including Amazon, Google and DHL. Their usage ranges from agriculture, aerial photography, traffic and ecology to surveillance and package delivery. Having the current trends and predictions about future use of this amazing technology in mind, it seems to be just a matter of time until we will alongside terrestrial (on the ground 2D) and aerial traffic (above 300 meters of altitude) have to control traffic of drones (below 300 meters of altitude). This zone of UAV traffic is currently not well regulated and often forbidden in many countries. The research in UAVTraffic is therefore aimed towards making a first step in developing basic
traffic rules for (autonomous) UAVs by using simulation models and small scale field studies. In accordance with this objective, UAVTraffic will make good use of advanced semantic Web technologies (especially of the ontology of cooperative intelligent transport systems developed at the University of Zagreb), multi-agent system methodology (especially proactive agents, automated planning and negotiation algorithms), technologies for machine vision (especially spatial object recognition), the Internet of Things or more specifically the Internet of Vehicles (a network of various devices like smart phones, watches, goggles, cars but also UAVs and other things that will allow for traffic control).

In many situations the use of an robot such as an flying drone are used to help compliment humans in performing tasks such as search and rescue. Often these robots require a human operator to control and oversee them. The use of an autonomous robot that can navigate in unknown environments autonomously removes the need for a human operator and allows for quicker task performing. These autonomous drones need to be able to identify objects and navigate an unknown space without the interaction from a human operator. This paper presents visual processing algorithms that can be used with an AR Drone quad copter to identify objects and colour, the paper also presents some search patterns as possible methods for autonomously searching unknown spaces. At the end of the paper some experiments are presented with their results on possible solutions for autonomous search and navigation.

The desire to make applications and machines more intelligent and the aspiration to enable their operation without human interaction have been driving innovations in neural networks, deep learning, and other machine learning techniques. Although reinforcement learning has been primarily used in video games, recent advancements and the development of diverse and powerful reinforcement algorithms have enabled the reinforcement learning community to move from playing video games to solving complex real-life problems in autonomous systems such as self-driving cars, delivery drones, and automated robotics. Understanding the environment of an application and the algorithms' limitations plays a vital role in selecting the appropriate reinforcement learning algorithm that successfully solves the problem on hand in an efficient manner. Consequently, in this study, we identify three main environment types and classify reinforcement learning algorithms according to those environment types. Moreover, within each category, we identify relationships between algorithms. The overview of each algorithm provides insight into the algorithms' foundations and reviews similarities and differences among algorithms. This study provides a perspective on the field and helps practitioners and researchers to select the appropriate algorithm for their use case.

This paper has been prepared for designing 4-rotor unmanned aerial vehicle (UAV) and carrying out
its control with PID controller. In this context, it divides into four fundamental parts. First part describes what
4-rotor UAV is. Second part is about mathematical model of the 4-rotor UAV. PID controlling and its basic
parameters have been analyzed theoretically in the third one. Finally, testing IMU sensors and some controlling
applications have been carried out and their outputs have been presented. As a result, the stabilization in desired
level has been obtained via designed PID controlling system.

In this paper, we propose an algorithm for the formation of multiple UAVs used in vision-based inspection of infrastructure. A path planning algorithm is first developed by using a variant of the particle swarm optimisation, named θ-PSO, to generate a feasible path for the overall formation configuration taken into account the constraints for visual inspection. Here, we introduced a cost function that includes various constraints on flight safety and visual inspection. A reconfigurable topology is then added based on the use of intermediate waypoints to allow the formation to avoid collision with obstacles during operation. The planned path and formation are then combined to derive the trajectory and velocity profiles for each UAV. Experiments have been conducted for the task of inspecting a light rail bridge. The results confirmed the validity and effectiveness of the proposed algorithm.

In this paper, a robust second order sliding mode control (SMC) for controlling a quadrotor with uncertain
parameters presented based on high order sliding mode control (HOSMC). A controller based on the
HOSMC technique is designed for trajectory tracking of a quadrotor helicopter with considering motor
dynamics. The main subsystems of quadrotor (i.e. position and attitude) stabilized using HOSMC method.
The performance and effectiveness of the proposed controller are tested in a simulation study taking into
account external disturbances with consider to motor dynamics. Simulation results show that the proposed
controller eliminates the disturbance effect on the position and attitude subsystems efficiency that can be
used in real time applications.

Unmanned Aerial Vehicles (UAVs), specifically
quadrotors, is one of the major topics of study in current
literature with applications to numerous fields. In this paper we
consider a test-bed for the design of a low cost flight controller
for a quadrotor and as a first step in design we demonstrate the
design of the roll controller on an experimental setup through
the stages of data collection, modeling, control design and
verification. The procedure consists of four stages: 1)
Experimental determination of controller coefficients, 2) Data
collection, 3) System identification, 4) Controller redesign by
tuning coefficients with a numerical search. It is observed that
the system designed as such is capable of achieving the desired
roll stabilization, and coefficient tuning on the identified model
noticeably improves the settling time and steady state oscillation
amplitude.

To date, most Sense-and-Avoid (SAA) architectures for Unmanned Aircraft Systems (UAS) have addressed only specific mission requirements and scenarios, whereas they shall support non-segregated operations in all airspace classes. The current line-of-sight/airspace restrictions are preventing further exploitations of UAS technology and impeding many practical uses. This paper proposes the development of a unified SAA analytical approach which integrates both non-cooperative sensors and cooperative systems. In the present work, the navigation and tracking errors are first considered and the required avoidance volume is obtained by applying inflations due to relative dynamics and wake turbulence effects. The model has a potential to be a foundation for the development of an airworthy SAA capability and can provide a pathway for manned/unmanned aircraft coexistence in all classes of airspace.

For last some decades unmanned aerial vehicles (UAV) are being used to a great extent in many
purposes like military surveillance, remote sensing, accident investigation etc. In recent years
UAVs are getting popular in inspection of structural damage specially after earth-quake like disasters.
Our quadcopter is controlled by RF joystick and equipped camera and an on-board controller.
Images captured by flying quadcopter with corresponding loaction have been processed to detect
the crack and damage condition. With available damage condition and location further steps can
be taken.

Unmanned Aerial Vehicles (UAVs) are currently one of
the hot topics of study which have numerous applications
such as remote sensing, aerial surveillance, exploration,
search and rescue, transport, scientific research and armed
attacks. In this work we consider a test-bed for the design
of a low cost flight controller for a quadrotor and we
demonstrate the design of the roll and pitch controllers on
an experimental setup through the stages of data
collection, modeling, control design and verification. The
procedure consists of four stages: 1) Experimental
determination of controller coefficients, 2) Data
collection, 3) System identification, 4) Controller redesign
by tuning coefficients with a numerical search. It is
observed that the system designed as such is capable of
achieving satisfactory pitch and roll stabilization, and
coefficient tuning on the identified model noticeably
improves the settling time and steady state oscillation
amplitude.

Animals, especially mammals like bats and dolphins, use acoustic waves that vary in frequency, signal duration, and intensity, for navigation and tracking. The directionality of acoustic waves has also been long used for localization by human beings. The term 'echolocation' was coined by Donald R. Griffin, where he discusses ship captains exploiting sound to ascertain the ship's surroundings and avoid obstacles in low-visibility environments. Acoustic sensors can provide a low-cost, size, weight, and power (C-SWaP) navigation solution, which is scalable and robust. Moreover, acoustic sensors have the capability to provide high-resolution spatial information at short distance range. This paper presents a novel acoustic positioning and navigation system for a micro aerial vehicle. Flight tests are performed to evaluate the system, where the performance of the acoustic system is compared with a motion capture system.

In this work, we propose a new design approach based on bat algorithm for optimal design of Fuzzy-Proportional-Derivative controllers (FPD) for the stabilization of a Quadrotor. To stabilize the angles (roll, pitch and yaw) and heights of the Quadrotor a decentralized control structure is adopted where four FPD controllers are used. Their parameters are given simultaneously by BAT algorithm. The performance of the system from its desired behavior is quantified by an objective function (SE). Some simulation results are presented to show the efficiency of the method .

TraQuad is an autonomous tracking quadcopter capable of tracking any moving (or static) object like cars, humans, other drones or any other object on-the-go. This article describes the applications and advantages of TraQuad and the reduction in cost (to about 250$) that has been achieved so far using the hardware and software capabilities and our custom algorithms wherever needed. This description is backed by strong data and the research analyses which have been drawn out of extant information or conducted on own when necessary. This also describes the development of completely autonomous (even GPS is optional) low-cost drone which can act as a major platform for further developments in automation, transportation, reconnaissance and more. We describe our ROS Gazebo simulator and our STATUS algorithms which form the core of our development of our object tracking drone for generic purposes.

This paper presents information about quadrocopter designed for special aim and its applications. In this context, it focuses basic subjects such as design of a quadrocopter, obtaining numerical data, detection of foreign matter on runway. The measurements and graphs belongs to the subject in point are included. The analyses to be carried out put forward that the quadrocopter has many fields of use contribute to safety of aircraft take-off and landing. Keywords—building of qudrocopter, telemetry, video processing

The current paper introduces the situated robotics competitions characteristics applied to robot soccer as the basis of the current subject, and exposes the development of a competition and experiment environment of situated robotics with the aiming on the use of a drone, in the context of high school and university student’s development.

Deep drone racing and navigation are emerging applications of deep learning which may be used in competitions and potentially to automatize a multitude of tasks accomplished by drones. In this paper, we apply the method Deep Deterministic Policy Gradient (DDPG) to train a neural network whose objective is to direct a simulated quadcopter towards a target, reproducing a simplified drone race environment. The model explored in the paper is not vision-based; it assumes the position and velocity of the drone in relation to the target are known at all times, and these variables are passed as inputs to the model. Based entirely on these variables, the neural network controls the quadcopter’s rotors angular speeds, which in turn determine the flight path taken by the drone. DDPG training requires engineering an efficient reward function, which is essential to the convergence of the model. A few different reward functions were tested and are presented in the paper. The results showed that DDPG is a suitable method for training a deep drone racing neural network, as suggested by the fact that, after training, the drone was able to make its way to the target within a certain range of initial distance and regardless of the initial directing vector from the drone to the target. In spite of that, certain minor problems remain to be solved and might be the subject of future works. Among those problems are the facts that the trajectories chosen by the neural network are generally not optimal and that the drone tends to diverge from the target as the former gets closer than a few feet to the latter.

En este artículo se presentan la línea de investigación relacionada con el vuelo autónomo de un cuadricóptero, empleando un sistema de visión externa en el contexto de la robótica situada. En este orden se emplea visión externa para capturar el ambiente de actuación, conformado por cuatro puntos o checkpoint que el sistema debe sobrevolar en forma autónoma, para el guiado a través de visión externa.

This paper focuses on robust wind disturbance rejection for nonlinear quadrotor models. By leveraging on nonlinear unknown observer theory, it proposes a nonlinear dynamic filter that, using sensors already on-board the aircraft, can estimate in real-time wind gust signals in the three dimensions. The wind disturbance is then treated as input to the PD controller for a quick and robust flight pathway in presence of disturbances. With this scheme, the wind disturbance can be precisely estimated online and compensated in real-time. Hence, the quadrotor can successfully reach its desired attitude and position. To show the effective and desired performance of the method, simulation results are presented in Matlab/Simulink and ROS-enabled Gazebo platform.