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  1. Swarm Intelligence
  2. Swarm Intelligence : Volume 10
  3. Swarm Intelligence : Volume 10, Issue 1, March 2016
  4. Adaptive foraging for simulated and real robotic swarms: the dynamical response threshold approach
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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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Special issue on swarm robotics

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Adaptive foraging for simulated and real robotic swarms: the dynamical response threshold approach

Content Provider SpringerLink
Author Castello, Eduardo Yamamoto, Tomoyuki Libera, Fabio Dalla Liu, Wenguo Winfield, Alan F. T. Nakamura, Yutaka Ishiguro, Hiroshi
Copyright Year 2015
Abstract Developing self-organised swarm systems capable of adapting to environmental changes as well as to dynamic situations is a complex challenge. An efficient labour division model, with the ability to regulate the distribution of work among swarm robots, is an important element of this kind of system. This paper extends the popular response threshold model and proposes a new adaptive response threshold model (ARTM). Experiments were carried out in simulation and in real-robot scenarios with the aim of studying the performance of this new adaptive model. Results presented in this paper verify that the extended approach improves on the adaptability of previous systems. For example, by reducing collision duration among robots in foraging missions, our approach helps small swarms of robots to adapt more efficiently to changing environments, thus increasing their self-sustainability (survival rate). Finally, we propose a minimal version of ARTM, which is derived from the conclusions drawn through real-robot and simulation results.
Starting Page 1
Ending Page 31
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 2016-01-02
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Adaptive foraging Cooperative behaviour Autonomous systems 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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