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  1. Swarm Intelligence
  2. Swarm Intelligence : Volume 8
  3. Swarm Intelligence : Volume 8, Issue 1, March 2014
  4. Cache consensus: rapid object sorting by a robotic swarm
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Swarm Intelligence : Volume 11
Swarm Intelligence : Volume 10
Swarm Intelligence : Volume 9
Swarm Intelligence : Volume 8
Swarm Intelligence : Volume 8, Issue 4, December 2014
Swarm Intelligence : Volume 8, Issue 3, September 2014
Swarm Intelligence : Volume 8, Issue 2, June 2014
Swarm Intelligence : Volume 8, Issue 1, March 2014
Cooperative navigation in robotic swarms
A generalized theoretical deterministic particle swarm model
Cache consensus: rapid object sorting by a robotic swarm
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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Cache consensus: rapid object sorting by a robotic swarm

Content Provider SpringerLink
Author Vardy, Andrew Vorobyev, Gregory Banzhaf, Wolfgang
Copyright Year 2014
Abstract We present a new method which allows a swarm of robots to sort arbitrarily arranged objects into homogeneous clusters. In the ideal case, a distributed robotic sorting method should establish a single homogeneous cluster for each object type. This can be achieved with existing methods, but the rate of convergence is considered too slow for real-world application. Previous research on distributed robotic sorting is typified by randomised movement with a pick-up/deposit behaviour that is a probabilistic function of local object density. We investigate whether the ability of each robot to localise and return to remembered places can improve distributed sorting performance. In our method, each robot maintains a cache point for each object type. Upon collecting an object, it returns to add this object to the cluster surrounding the cache point. Similar to previous biologically inspired work on distributed sorting, no explicit communication between robots is implemented. However, the robots can still come to a consensus on the best cache for each object type by observing clusters and comparing their sizes with remembered cache sizes. We refer to this method as cache consensus. Our results indicate that incorporating this localisation capability enables a significant improvement in the rate of convergence. We present experimental results using a realistic simulation of our targeted robotic platform. A subset of these experiments is also validated on physical robots.
Starting Page 61
Ending Page 87
Page Count 27
File Format PDF
ISSN 19353812
Journal Swarm Intelligence
Volume Number 8
Issue Number 1
e-ISSN 19353820
Language English
Publisher Springer US
Publisher Date 2014-01-24
Publisher Place Boston
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Swarm robotics Patch sorting Clustering Localisation 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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