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
Steering self-organized robot flocks through externally guided individuals
Date
2010-09-01
Author
Celikkanat, Hande
Şahin, Erol
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
179
views
0
downloads
Cite This
In this paper, we study how a self-organized mobile robot flock can be steered toward a desired direction through externally guiding some of its members. Specifically, we propose a behavior by extending a previously developed flocking behavior to steer self-organized flocks in both physical and simulated mobile robots. We quantitatively measure the performance of the proposed behavior under different parameter settings using three metrics, namely, (1) the mutual information metric, adopted from Information Theory, to measure the information shared between the individuals during steering, (2) the accuracy metric from directional statistics to measure the angular deviation of the direction of the flock from the desired direction, and (3) the ratio of the largest aggregate to the whole flock and the ratio of informed individuals remaining with the largest aggregate, as a metric of flock cohesion. We conducted a systematic set of experiments using both physical and simulated robots, analyzed the transient and steady-state characteristics of steered flocking, and evaluate the parameter conditions under which a swarm can be successfully steered. We show that the experimental results are qualitatively in accordance with the ones that were predicted in Couzin et al. model (Nature, 433:513-516, 2005) and relate the quantitative differences to the differences between the models.
Subject Keywords
Software
,
Artificial Intelligence
URI
https://hdl.handle.net/11511/40754
Journal
NEURAL COMPUTING & APPLICATIONS
DOI
https://doi.org/10.1007/s00521-010-0355-y
Collections
Department of Computer Engineering, Article
Suggestions
OpenMETU
Core
Learning Context on a Humanoid Robot using Incremental Latent Dirichlet Allocation
Çelikkanat, Hande; Orhan, Guner; Pugeault, Nicolas; Guerin, Frank; Şahin, Erol; Kalkan, Sinan (Institute of Electrical and Electronics Engineers (IEEE), 2016-03-01)
In this paper, we formalize and model context in terms of a set of concepts grounded in the sensorimotor interactions of a robot. The concepts are modeled as a web using Markov Random Field (MRF), inspired from the concept web hypothesis for representing concepts in humans. On this concept web, we treat context as a latent variable of Latent Dirichlet Allocation (LDA), which is a widely-used method in computational linguistics for modeling topics in texts. We extend the standard LDA method in order to make ...
Control of underactuated planar pronking through an embedded spring-mass Hopper template
Ankaralı, Mustafa Mert; Saranlı, Uluç (Springer Science and Business Media LLC, 2011-02-01)
Autonomous use of legged robots in unstructured, outdoor settings requires dynamically dexterous behaviors to achieve sufficient speed and agility without overly complex and fragile mechanics and actuation. Among such behaviors is the relatively under-studied pronking (aka. stotting), a dynamic gait in which all legs are used in synchrony, usually resulting in relatively slow speeds but long flight phases and large jumping heights. Instantiations of this gait for robotic systems have been mostly limited to ...
Guiding a Robot Flock via Informed Robots
Celikkanat, Hande; Turgut, Ali Emre; Şahin, Erol (2008-11-19)
In this paper, we study how and to what extent a self-organized mobile robot flock can be guided to move in a desired direction by informing some of the individuals within the flock. Specifically, we extend a flocking behavior that was shown to maneuver a swarm of mobile robots as a cohesive group in free space avoiding obstacles in its path. In its original form, this behavior does not have a preferred direction and the flock would wander aimlessly in the environment. In this study, we extend the flocking ...
A Hopfield neural network with multi-compartmental activation
Akhmet, Marat (Springer Science and Business Media LLC, 2018-05-01)
The Hopfield network is a form of recurrent artificial neural network. To satisfy demands of artificial neural networks and brain activity, the networks are needed to be modified in different ways. Accordingly, it is the first time that, in our paper, a Hopfield neural network with piecewise constant argument of generalized type and constant delay is considered. To insert both types of the arguments, a multi-compartmental activation function is utilized. For the analysis of the problem, we have applied the ...
AN EFFICIENT DATABASE TRANSITIVE CLOSURE ALGORITHM
Toroslu, İsmail Hakkı; HENSCHEN, L (Springer Science and Business Media LLC, 1994-05-01)
The integration of logic rules and relational databases has recently emerged as an important technique for developing knowledge management systems. An important class of logic rules utilized by these systems is the so-called transitive closure rules, the processing of which requires the computation of the transitive closure of database relations referenced by these rules. This article presents a new algorithm suitable for computing the transitive closure of very large database relations. This algorithm proc...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
H. Celikkanat and E. Şahin, “Steering self-organized robot flocks through externally guided individuals,”
NEURAL COMPUTING & APPLICATIONS
, pp. 849–865, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40754.