Drone Tracking with Drone using Deep Learning

With the development of technology, studies in fields such as artificial intelligence, computer vision and deep learning are increasing day by day. In line with these developments, object tracking and object detection studies have spread over wide areas. In this article, a study is presented by simulating two different drones, a leader and a follower drone, accompanied by deep learning algorithms. Within the scope of this study, it is aimed to perform a drone tracking with drone in an autonomous way. Two different approaches are developed and tested in the simulator environment within the scope of drone tracking. The first of these approaches is to enable the leader drone to detect the target drone by using object-tracking algorithms. YOLOv5 deep learning algorithm is preferred for object detection. A data set of approximately 2500 images was created for training the YOLOv5 algorithm. The Yolov5 object detection algorithm, which was trained with the created data set, reached a success rate of approximately 93% as a result of the training. As the second approach, the object-tracking algorithm we developed is used. Trainings were carried out in the simulator created in the Matlab environment. The results are presented in detail in the following sections. In this article, some artificial neural networks and some object tracking methods used in the literature are explained

How hard is it to cross the room? -- Training (Recurrent) Neural Networks to steer a UAV

This work explores the feasibility of steering a drone with a (recurrent) neural network, based on input from a forward looking camera, in the context of a high-level navigation task. We set up a generic framework for training a network to perform navigation tasks based on imitation learning. It can be applied to both aerial and land vehicles. As a proof of concept we apply it to a UAV (Unmanned Aerial Vehicle) in a simulated environment, learning to cross a room containing a number of obstacles. So far only feedforward neural networks (FNNs) have been used to train UAV control. To cope with more complex tasks, we propose the use of recurrent neural networks (RNN) instead and successfully train an LSTM (Long-Short Term Memory) network for controlling UAVs. Vision based control is a sequential prediction problem, known for its highly correlated input data. The correlation makes training a network hard, especially an RNN. To overcome this issue, we investigate an alternative sampling method during training, namely window-wise truncated backpropagation through time (WW-TBPTT). Further, end-to-end training requires a lot of data which often is not available. Therefore, we compare the performance of retraining only the Fully Connected (FC) and LSTM control layers with networks which are trained end-to-end. Performing the relatively simple task of crossing a room already reveals important guidelines and good practices for training neural control networks. Different visualizations help to explain the behavior learned.Comment: 12 pages, 30 figure

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics

A Review of Consensus-based Multi-agent UAV Applications

In this paper, a review of distributed control for multi-agent systems is proposed, focusing on consensus-based applications. Both rotary-wing and fixed-wing Unmanned Aerial Vehicles (UAVs) are considered. On one side, methodologies and implementations based on collision and obstacle avoidance through consensus are analyzed for multirotor UAVs. On the other hand, a target tracking through consensus is considered for fixed-wing UAVs. This novel approach to classify the literature could help researchers to assess the outcomes achieved in these two directions in view of potential practical implementations of consensus-based methodologies

Formation of a Wireless Communication System Based on a Swarm of Unmanned Aerial Vehicles

Проблематика. На даний час бурхливо розвивається новий напрямок в техніці рухомих систем, пов'язаний із застосуванням множини/групи рухомих багатофункціональних вузлів, які можуть створювати різні просторово-розподілені структури для різних застосувань: від розважальних шоу, до розвідувальної мережі. Йдеться про техніку малих безпілотних літальних апаратів(БЛА), частіше званих дронами, та їх використання в області побудови телекомунікаційних систем. Мета. Метою роботи є розробка основних принципів і стратегій для формування неоднорідної безпроводової системи зв'язку на базі рою безпілотних літаючих апаратів. Методи. Досліджуються структурно-функціональні методи побудови безпроводової мережі. Результати. Представлені сценарії централізованої і розподіленої побудови безпроводової мережі керування рою БЛА, проведена оцінка ускладнення функціональності вузлів рою в разі розподіленого сценарію. Розроблено схему поетапної реалізації життєвого циклу рою БЛА для послуг зв'язку. Представлений«молекулярний» сценарій просторової самоорганізації дронів-вузлів рою, який може бути реалізований за допомогою процедур«ланцюжка» і«спалаху». Запропоновано побудови деяких стратегій управління роєм: централізоване і децентралізоване з Ведучим, колективне само керування з обміном інформацією, децентралізоване керування з прогнозуванням, самоорганізація без обміну інформацією. Висновки. Розроблено основні принципи і стратегії формування неоднорідної безпроводової системи зв'язку на базі рою безпілотних літаючих апаратів. Розроблено стратегію колективного управління роєм дронів.Background. Currently, a new direction in the technology of mobile systems is rapidly developing, associated with the use of a set / group of mobile multifunctional elements that can create different spatially-distributed structures for various applications: from entertainment shows to intelligence networks. This is a technique of small unmanned aerial vehicles (UAV), often called drones, and their use in the field of building telecommunication systems. Objective. The aim of the work is to develop the basic principles and strategies for the formation of a heterogeneous wireless communication system based on a swarm of unmanned aerial vehicles. Methods. We study the structural and functional methods of building a wireless network. Results. Scenarios of centralized and distributed building of a wireless network of control of a swarm of UAVs are presented, assessment of the complexity of the functionality of swarm nodes inthe case of a distributed scenario is carried out. A schemeof phased implementation of the life cycle of a UAV swarm for communication services has been developed. The “molecular” scenario of spatial self-organization of the swarm-nodes of the swarm is presented, which can beimplemented using the “chain” and “flash” procedures. The proposed construction of some strategies for managing the swarm: centralized and decentralized with the Leader, collective self-management with information sharing,decentralized management withforecasting, self-organization without information sharing. Conclusions. The basic principles and strategies for the formation of a heterogeneous wireless communication system based on a swarm of unmanned aerial vehicles have been developed. A collective management strategy for a swarm of drones was developed. Keywords:swarm of unmanned aerial vehicles; drone swarm; communication system; life cycle; control network.Проблематика. В настоящее время очень бурно развивается новое направление в технике подвижных систем, связанное с применением множества/группы подвижных многофункциональных элементов, которые могут создавать различные пространственно-распределенные структуры для различных применений: от развлекательных шоу, до разведывательной сети. Речь идет о технике малых беспилотных летающих аппаратов(БЛА), чаще называемых дронами, и их использование в области построения телекоммуникационных систем. Цель. Целью работы является разработка основных принципов и стратегий для формирования неоднородной беспроводной системы связи на базе роя беспилотных летающих аппаратов. Методы. Исследуются структурно-функциональные методы построения беспроводной сети. Результаты. Представлены сценарии централизованного и распределенного построения беспроводной сети управления роя БЛА, проведена оценка усложнения функциональности узлов роя в случае распределенного сценария. Разработана схема поэтапной реализации жизненного цикла роя БЛА для услуг связи. Представлен«молекулярный» сценарий пространственной самоорганизации дронов-узлов роя, который может быть реализован посредством процедур