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  1. Swarm Intelligence
  2. Swarm Intelligence : Volume 10
  3. Swarm Intelligence : Volume 10, Issue 1, March 2016
  4. Information flow principles for plasticity in foraging robot swarms
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Swarm Intelligence : Volume 11
Swarm Intelligence : Volume 10
Swarm Intelligence : Volume 10, Issue 4, December 2016
Swarm Intelligence : Volume 10, Issue 3, September 2016
Swarm Intelligence : Volume 10, Issue 2, June 2016
Swarm Intelligence : Volume 10, Issue 1, March 2016
Adaptive foraging for simulated and real robotic swarms: the dynamical response threshold approach
Information flow principles for plasticity in foraging robot swarms
Supervisory control theory applied to swarm robotics
Swarm Intelligence : Volume 9
Swarm Intelligence : Volume 8
Swarm Intelligence : Volume 7
Swarm Intelligence : Volume 6
Swarm Intelligence : Volume 5
Swarm Intelligence : Volume 4
Swarm Intelligence : Volume 3
Swarm Intelligence : Volume 2
Swarm Intelligence : Volume 1

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Information flow principles for plasticity in foraging robot swarms

Content Provider SpringerLink
Author Pitonakova, Lenka Crowder, Richard Bullock, Seth
Copyright Year 2016
Abstract An important characteristic of a robot swarm that must operate in the real world is the ability to cope with changeable environments by exhibiting behavioural plasticity at the collective level. For example, a swarm of foraging robots should be able to repeatedly reorganise in order to exploit resource deposits that appear intermittently in different locations throughout their environment. In this paper, we report on simulation experiments with homogeneous foraging robot teams and show that analysing swarm behaviour in terms of information flow can help us to identify whether a particular behavioural strategy is likely to exhibit useful swarm plasticity in response to dynamic environments. While it is beneficial to maximise the rate at which robots share information when they make collective decisions in a static environment, plastic swarm behaviour in changeable environments requires regulated information transfer in order to achieve a balance between the exploitation of existing information and exploration leading to acquisition of new information. We give examples of how information flow analysis can help designers to decide on robot control strategies with relevance to a number of applications explored in the swarm robotics literature.
Starting Page 33
Ending Page 63
Page Count 31
File Format PDF
ISSN 19353812
Journal Swarm Intelligence
Volume Number 10
Issue Number 1
e-ISSN 19353820
Language English
Publisher Springer US
Publisher Date 2016-02-09
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Swarm robotics Foraging Communication Self-organisation Plasticity Artificial Intelligence (incl. Robotics) Computer Systems Organization and Communication Networks ApplicationMathematics/Computational Methods of Engineering Communications Engineering, Networks Computer Communication Networks
Content Type Text
Resource Type Article
Subject Artificial Intelligence
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