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  1. Swarm Intelligence
  2. Swarm Intelligence : Volume 5
  3. Swarm Intelligence : Volume 5, Issue 1, March 2011
  4. Swarm cognition on off-road autonomous robots
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Swarm Intelligence : Volume 11
Swarm Intelligence : Volume 10
Swarm Intelligence : Volume 9
Swarm Intelligence : Volume 8
Swarm Intelligence : Volume 7
Swarm Intelligence : Volume 6
Swarm Intelligence : Volume 5
Swarm Intelligence : Volume 5, Issue 3-4, December 2011
Swarm Intelligence : Volume 5, Issue 2, June 2011
Swarm Intelligence : Volume 5, Issue 1, March 2011
Special issue on Swarm Cognition
Swarm Cognition: an interdisciplinary approach to the study of self-organising biological collectives
Termites as models of swarm cognition
Swarm cognition on off-road autonomous robots
Swarm Intelligence : Volume 4
Swarm Intelligence : Volume 3
Swarm Intelligence : Volume 2
Swarm Intelligence : Volume 1

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Swarm cognition on off-road autonomous robots

Content Provider SpringerLink
Author Santana, Pedro Correia, Luís
Copyright Year 2010
Abstract This paper contributes with the first validation of swarm cognition as a useful framework for the design of autonomous robots controllers. The proposed model is built upon the authors’ previous work validated on a simulated robot performing local navigation on a 2-D deterministic world. Based on the ant foraging metaphor and motivated by the multiple covert attention hypothesis, the model consists of a set of simple virtual agents inhabiting the robot’s visual input, searching in a collectively coordinated way for obstacles. Parsimonious and accurate visual attention, operating on a by-need basis, is attained by making the activity of these agents modulated by the robot’s action selection process. A by-product of the system is the maintenance of active, parallel and sparse spatial working memories. In short, the model exhibits the self-organisation of a relevant set of features composing a cognitive system. To show its robustness, the model is extended in this paper to handle the challenges of physical off-road robots equipped with noisy stereoscopic vision sensors. Furthermore, an extensive aggregate of biological arguments sustaining the model is provided. Experimental results show the ability of the model to robustly control the robot on a local navigation task, with less than 1% of the robot’s visual input being analysed. Hence, with this system the computational cost of perception is considerably reduced, thus fostering robot miniaturisation and energetic efficiency. This confirms the advantages of using a swarm-based system, operating in an intricate way with action selection, to judiciously control visual attention and maintain sparse spatial memories, constituting a basic form of swarm cognition.
Starting Page 45
Ending Page 72
Page Count 28
File Format PDF
ISSN 19353812
Journal Swarm Intelligence
Volume Number 5
Issue Number 1
e-ISSN 19353820
Language English
Publisher Springer US
Publisher Date 2011-01-04
Publisher Place Boston
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Swarm cognition Visual attention Spatial memory Action selection Autonomous robots Biological inspiration Computer Communication Networks Communications Engineering, Networks ApplicationMathematics/Computational Methods of Engineering Computer Systems Organization and Communication Networks Artificial Intelligence (incl. Robotics)
Content Type Text
Resource Type Article
Subject Artificial Intelligence
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