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  1. Swarm Intelligence
  2. Swarm Intelligence : Volume 5
  3. Swarm Intelligence : Volume 5, Issue 2, June 2011
  4. Self-organized cooperation between robotic swarms
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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
Self-organized cooperation between robotic swarms
Self-organised path formation in a swarm of robots
Deciding on the wing: in-flight decision making and search space sampling in the red dwarf honeybee Apis florea
Swarm Intelligence : Volume 5, Issue 1, March 2011
Swarm Intelligence : Volume 4
Swarm Intelligence : Volume 3
Swarm Intelligence : Volume 2
Swarm Intelligence : Volume 1

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Self-organized cooperation between robotic swarms

Content Provider SpringerLink
Author Ducatelle, Frederick Di Caro, Gianni A. Pinciroli, Carlo Gambardella, Luca M.
Copyright Year 2011
Abstract We study self-organized cooperation between heterogeneous robotic swarms. The robots of each swarm play distinct roles based on their different characteristics. We investigate how the use of simple local interactions between the robots of the different swarms can let the swarms cooperate in order to solve complex tasks. We focus on an indoor navigation task, in which we use a swarm of wheeled robots, called foot-bots, and a swarm of flying robots that can attach to the ceiling, called eye-bots. The task of the foot-bots is to move back and forth between a source and a target location. The role of the eye-bots is to guide foot-bots: they choose positions at the ceiling and from there give local directional instructions to foot-bots passing by. To obtain efficient paths for foot-bot navigation, eye-bots need on the one hand to choose good positions and on the other hand learn the right instructions to give. We investigate each of these aspects. Our solution is based on a process of mutual adaptation, in which foot-bots execute instructions given by eye-bots, and eye-bots observe the behavior of foot-bots to adapt their position and the instructions they give. Our approach is inspired by pheromone mediated navigation of ants, as eye-bots serve as stigmergic markers for foot-bot navigation. Through simulation, we show how this system is able to find efficient paths in complex environments, and to display different kinds of complex and scalable self-organized behaviors, such as shortest path finding and automatic traffic spreading.
Starting Page 73
Ending Page 96
Page Count 24
File Format PDF
ISSN 19353812
Journal Swarm Intelligence
Volume Number 5
Issue Number 2
e-ISSN 19353820
Language English
Publisher Springer US
Publisher Date 2011-03-18
Publisher Place Boston
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Swarm robotics Heterogeneous robot swarms Swarm intelligence Self-organization Stigmergy Robot navigation Multi-robot systems Ant foraging Computer Systems Organization and Communication Networks Artificial Intelligence (incl. Robotics) Computer Communication Networks Communications Engineering, Networks ApplicationMathematics/Computational Methods of Engineering
Content Type Text
Resource Type Article
Subject Artificial Intelligence
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