Çoklu Hedeflerin Çoklu Robotlara Paylaştırılması İçin Bir Yük Dengeleme Sistemi

Dönmez, Emrah; Kocamaz, Adnan Fatih

doi:10.17798/bitlisfen.467757

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Today, systems made with a single robot dominate robotic fields such as path planning and controller design. There are fewer studies on multiple robots and multi-target/task sharing [1]. Unmanned aerial vehicle (UAV) is an aircraft that can be controlled remotely [2].…”

Section: Introductionmentioning

confidence: 99%

Calculation Of The Optimum Number Of Unmanned Air Vehicles Required For Surveillance Missions

ÖZDEMİR

Çaşka

2022

Academic Platform Journal of Engineering and Smart Systems

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The aim of this study is to determine minimum quantity of unmanned aerial vehicles (UAVs) that should be used in an area where an aerial reconnaissance/observation activity will be carried out. In order to make this kind of calculation, firstly, the energy consumption of a UAV while flying with a constant speed was examined and then % energy level consumed by the UAV while passing each meter was obtained approximately. In this study, by considering the length of the trajectory which a UAV will navigate, required % energy level of a UAV to complete a single tour is calculated. If it is determined that one UAV can not complete its assigned trajectory, the number of UAVs are increased until each UAV complete its trajectory. In this study, the vehicle routing problem approach was used to calculate the UAV trajectories. Genetic algorithm method that is one of the metaheuristic optimization methods, was used obtain the solution of the vehicle routing problem (VRP). The developed algorithm has been run in Matlab environment. By changing the parameters of crossing rate and the population number in the genetic algorithm (GA) method, the lowest number of UAVs that is enough to carry out aerial observation over a target area and the shortest UAV trajectories were obtained. Results were presented in table form.

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Section: Introductionmentioning

confidence: 99%

Calculation Of The Optimum Number Of Unmanned Air Vehicles Required For Surveillance Missions

ÖZDEMİR

Çaşka

2022

Academic Platform Journal of Engineering and Smart Systems

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“…problems existing in the traditional routing planning system [4]. Researchers such as Abhijit A proposed an adaptive variable neighborhood search ACO to solve the vehicle routing problem with soft time window.…”

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confidence: 99%

Distributed System Task Assignment Considering Ant Colony Algorithm

2022

DPS

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In a company of a certain scale, a large number of tasks need to be allocated every day. It is particularly important to provide a comprehensive and efficient distributed system for resource scheduling. Ant colony algorithm (ACO) has great advantages in resource scheduling, so In this paper, the problem of task assignment in distributed systems is studied considering the ant colony optimization algorithm. The structure of this paper can be divided into three parts, including related theoretical overview, system design and system analysis. In the system design and system analysis, through the comparative analysis of the ant colony algorithm (ACO), the ant colony optimization algorithm based on load balancing (LBACO) and the improved ant colony algorithm (IACO), the optimal algorithm is found. Assign tasks. 1.IntroductionWith the rapid development of computer and information technology and its in-depth integration with traditional technologies in various industries, the data transmitted by task allocation is huge, which will inevitably increase the load pressure of distributed systems. An efficient and reasonable resource scheduling scheme will meet the needs of users At the same time of demand, resources can also be used to the fullest, avoiding the situation that resources are wasted due to idleness or time-consuming due to waiting [1][2]. ACO was initially used to solve the traveling salesman problem by virtue of its good performance in finding optimal paths, and was gradually used in solving task scheduling problems [3].At present, many researchers have carried out in-depth research on ACO and task allocation in distributed systems, and have achieved good results. For example, scholars such as Dnmez E proposed an intelligent planning (IP) system for scenic routes based on ACO. The software part of the system uses ACO to convert the landscape route planning problem into the shortest feasible route problem, and calculates the transition probability of ants to scenic spots (SS) by calculating the transition probability of ants to SS. Construct the weight matrix path search used by ants in this process, build an IP model, and realize the IP of SS. The results show that the system can solve the

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“…Dönmez ve Kocamaz, yaptıkları çalışmada birden fazla robot ile birden fazla hedefin gezilmesi bu durumlarına göre görev paylaşımı yapılması ve yük dengeleme sistemini gerçekleştirmiştir. Yapılan görev dağılımı yük dengeleme sisteminin aktif veya pasif olma durumlarına göre gerçekleştirilmiştir [3]. Çayırpunar, yaptığı çalışmada birden fazla robotun bir arama görevinde haberleştirilmesi işlemini gerçekleştirmiştir [4].…”

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Endüstriyel ortamda çalışan robotların görev paylaşımı için görüntü işleme tabanlı sistem önerisi

Ak¹,

Dereli²

2021

acperpro

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Bu çalışmada aynı otomasyon hattında farklı biçimde ve türde üretilen parçaların tespit edilmesi ve ayrıştırılması işlemi için birden fazla robot kolun görev paylaşımı için görüntü işleme temelli bir yöntem önerilmektedir. Yüksek hızda ve doğrulukta üretim, insan kaynaklı hataları en aza indirgeme ve insan gücü yerine makine gücü kullanma gibi avantajların birden fazla robot kol kullanarak robotların iş birliği ile yaptırılıyor olması bu avantajları en üst seviyeye çekmektedir. Bunun için çok sayıda robot kolun bulunduğu iş hattında her bir robot kolun seçmesi gereken nesneyi görüntü işleme ile tespit edilen nesne özelliklerine göre robot kollara atanması işlemi gerçekleştirilmiştir. Birden fazla robot kol kullanımı ile robotların iş birliği anlamında önemli olan bu çalışmanın ön hazırlık çalışması Matlab GUI arabiriminde gerçekleştirilmiştir. Oluşturulan kullanıcı arayüzü ile kullanıcı tabanlı renk, şekil ve boyut seçimi sonucunda parça tespit işlemi yaptırılabilmektedir.

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Çoklu Hedeflerin Çoklu Robotlara Paylaştırılması İçin Bir Yük Dengeleme Sistemi

Cited by 4 publications

References 17 publications

Calculation Of The Optimum Number Of Unmanned Air Vehicles Required For Surveillance Missions

Calculation Of The Optimum Number Of Unmanned Air Vehicles Required For Surveillance Missions

Distributed System Task Assignment Considering Ant Colony Algorithm

Endüstriyel ortamda çalışan robotların görev paylaşımı için görüntü işleme tabanlı sistem önerisi

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