Particle Swarm Optimization in Swarm Robotics

Turkler, Levent; Akkan, L. Ozlem; Akkan, Taner

doi:10.1109/hora49412.2020.9152861

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Moving a single robot is usually easier than moving multiple robots. In this study, research has been done on the problems that can be experienced when it is needed to move the swarm together, and optimization suggestions are presented according to these problems [10].…”

Section: Design Problems In a Swarm Roboticsmentioning

confidence: 99%

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Usage of Evolutionary Algorithms in Swarm Robotics and Design Problems

Turkler

Akkan

AKKAN>

2022

Sensors

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In this study, the general structure of swarm robotics is examined. Algorithms inspired by nature, which form the basis of swarm robotics, are introduced. Communication topologies in robotic swarms, which are similar to the communication methods between living things moving in nature, are included and how these can be used in swarm communication is emphasized. With the developed algorithms, how the swarm can imitate nature and what tasks it can perform have been explained. The various problems that will be encountered in terms of the design of the optimization methods used during the control of the swarm and the solutions are simulated using the Webots software. As a result, ideas on the solutions of these problems and suggestions are proposed.

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Section: Design Problems In a Swarm Roboticsmentioning

confidence: 99%

“…It is not a correct point of view to look at this phenomenon as central management or leadership. Providing the best movement for the group is achieved by imitating the individual in the most suitable position by all members of the group [10]. Is it possible to transfer this ability of living things or nature to swarm robotics?…”

Section: Introductionmentioning

confidence: 99%

Usage of Evolutionary Algorithms in Swarm Robotics and Design Problems

Turkler

Akkan

AKKAN>

2022

Sensors

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A Detailed Review of Swarm Robotics and its Significance

Rooban

Javaraiu

et al. 2022

2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)

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Adaptive Learning and Self-Organization in Swarm Robotics

Acharya,

Das,

Kitmo

2024

Bio-Inspired Swarm Robotics and Control

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This study explores neural architecture mapping (NAM) in human-swarm interaction (HSI), merging neuroscience, computer science, and robotics to enhance collaboration. It discusses NAM's concepts, challenges, and applications like neuroanatomy mapping, neural interface design, and algorithm development. Despite hurdles like neural complexity, NAM promises real-time monitoring, advanced brain-machine interfaces, and seamless HSI integration. The chapter offers an overview of NAM's aims, methods, and impacts on human-robot symbiosis and swarm systems. Theoretical neuroscience, human factor studies, and swarm robotics inform NAM, integrating neural networks with robotics for AI-driven swarm behavior. Adaptive learning in swarm robotics develops autonomous algorithms, enhancing collaboration. This research aims to improve swarm robotics' efficiency across various domains.

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Particle Swarm Optimization in Swarm Robotics

Cited by 3 publications

References 12 publications

Usage of Evolutionary Algorithms in Swarm Robotics and Design Problems

Usage of Evolutionary Algorithms in Swarm Robotics and Design Problems

A Detailed Review of Swarm Robotics and its Significance

Adaptive Learning and Self-Organization in Swarm Robotics

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