Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Evolving self-organizing behaviors for a swarm-bot
Download
index.pdf
Date
2004-09-01
Author
Dorigo, M
Trianni, V
Şahin, Erol
Gross, R
Labella, TH
Baldassarre, G
Nolfi, S
Deneubourg, JL
Mondada, F
Floreano, D
Gambardella, LM
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
201
views
199
downloads
Cite This
In this paper, we introduce a self-assembling and self-organizing artifact, called a swarm-bot, composed of a swarm of s-bots, mobile robots with the ability to connect to and to disconnect from each other. We discuss the challenges involved in controlling a swarm-bot and address the problem of synthesizing controllers for the swarm-bot using artificial evolution. Specifically, we study aggregation and coordinated motion of the swarm-bot using a physics-based simulation of the system. Experiments, using a simplified simulation model of the s-bots, show that evolution can discover simple but effective controllers for both the aggregation and the coordinated motion of the swarm-bot. Analysis of the evolved controllers shows that they have properties of scalability, that is, they continue to be effective for larger group sizes, and of generality, that is, they produce similar behaviors for configurations different from those they were originally evolved for. The portability of the evolved controllers to real s-bots is tested using a detailed simulation model which has been validated against the real s-bots in a companion paper in this same special issue.
Subject Keywords
Evolutionary robotics
,
Swarm-bot
,
Swarm intelligence
,
Swarm robotics
URI
https://hdl.handle.net/11511/47139
Journal
AUTONOMOUS ROBOTS
DOI
https://doi.org/10.1023/b:auro.0000033973.24945.f3
Collections
Department of Computer Engineering, Article
Suggestions
OpenMETU
Core
Evolving aggregation behaviors in a swarm of robots
Trianni, V; Gross, R; Labella, TH; Şahin, Erol; Dorigo, M (2003-01-01)
In this paper, we study aggregation in a swarm of simple robots, called s-bots, having the capability to self-organize and self-assemble to form a robotic system, called a swarm-bot. The aggregation process, observed in many biological systems, is of fundamental importance since it is the prerequisite for other forms of cooperation that involve self-organization and self-assembling. We consider the problem of defining the control system for the swarm-bot using artificial evolution. The results obtained in a...
DEVELOPMENT OF A SOCIAL REINFORCEMENT LEARNING BASED AGGREGATION METHOD WITH A MOBILE ROBOT SWARM
Gür, Emre; Turgut, Ali Emre; Şahin, Erol; Department of Mechanical Engineering (2022-9-09)
In this thesis, the development of a social, reinforcement learning-based aggregation method is covered together with the development of a mobile robot swarm of Kobot- Tracked (Kobot-T) robots. The proposed method is developed to improve efficiency in low robot density swarm environments especially when the aggregated area is difficult to find. The method is called Social Reinforcement Learning, and Landmark-Based Aggregation (SRLA) and it is based on Q learning. In this method, robots share their Q tables ...
A self-organized collective foraging method using a robot swarm
Karagüzel, Tugay Alperen; Turgut, Ali Emre; Department of Mechanical Engineering (2020)
In this thesis, a collective foraging method for a swarm of aerial robots is investigated. The method is constructed by using algorithms that are designed to work in a distributed manner, by using only local information. No member in the swarm has access to global information about positions, states or environment. The environment, that robots are planned to operate in, contains a virtual scalar field which consists of grids containing constant values. The grid values indicate desired regions of the environ...
GESwarm Grammatical Evolution for the Automatic Synthesis of Collective Behaviors in Swarm Robotics
Ferrante, Eliseo; Turgut, Ali Emre; DuenezGuzman, Edgar; Wenseleers, Tom (2013-07-10)
In this paper we propose GESwarm, a novel tool that can automatically synthesize collective behaviors for swarms of autonomous robots through evolutionary robotics. Evolutionary robotics typically relies on artificial evolution for tuning the weights of an artificial neural network that is then used as individual behavior representation. The main caveat of neural networks is that they are very difficult to reverse engineer, meaning that once a suitable solution is found, it is very difficult to analyze, to ...
Self-organized flocking in mobile robot swarms
Turgut, Ali Emre; Gökçe, Fatih; Şahin, Erol (2008-09-01)
In this paper, we study self-organized flocking in a swarm of mobile robots. We present Kobot, a mobile robot platform developed specifically for swarm robotic studies. We describe its infrared-based short range sensing system, capable of measuring the distance from obstacles and detecting kin robots, and a novel sensing system called the virtual heading system (VHS) which uses a digital compass and a wireless communication module for sensing the relative headings of neighboring robots. We propose a behavi...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. Dorigo et al., “Evolving self-organizing behaviors for a swarm-bot,”
AUTONOMOUS ROBOTS
, pp. 223–245, 2004, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47139.