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  1. Swarm Intelligence
  2. Swarm Intelligence : Volume 3
  3. Swarm Intelligence : Volume 3, Issue 3, September 2009
  4. Distributed scalable multi-robot learning using particle swarm optimization
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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 4
Swarm Intelligence : Volume 3
Swarm Intelligence : Volume 3, Issue 4, December 2009
Swarm Intelligence : Volume 3, Issue 3, September 2009
Metaheuristics for the bi-objective orienteering problem
Distributed scalable multi-robot learning using particle swarm optimization
Estimation-based ant colony optimization and local search for the probabilistic traveling salesman problem
Swarm Intelligence : Volume 3, Issue 2, June 2009
Swarm Intelligence : Volume 3, Issue 1, March 2009
Swarm Intelligence : Volume 2
Swarm Intelligence : Volume 1

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Distributed scalable multi-robot learning using particle swarm optimization

Content Provider SpringerLink
Author Pugh, Jim Martili, Alcherio
Copyright Year 2009
Abstract Designing effective behavioral controllers for mobile robots can be difficult and tedious; this process can be circumvented by using online learning techniques which allow robots to generate their own controllers online in an automated fashion. In multi-robot systems, robots operating in parallel can potentially learn at a much faster rate by sharing information amongst themselves. In this work, we use an adapted version of the Particle Swarm Optimization algorithm in order to accomplish distributed online robotic learning in groups of robots with access to only local information. The effectiveness of the learning technique on a benchmark task (generating high-performance obstacle avoidance behavior) is evaluated for robot groups of various sizes, with the maximum group size allowing each robot to individually contain and manage a single PSO particle. To increase the realism of the technique, different PSO neighborhoods based on limitations of real robotic communication are tested and compared in this scenario. We explore the effect of varying communication power for one of these communication-based PSO neighborhoods. To validate the effectiveness of these learning techniques, fully distributed online learning experiments are run using a group of 10 real robots, generating results which support the findings from our simulations.
Starting Page 203
Ending Page 222
Page Count 20
File Format PDF
ISSN 19353812
Journal Swarm Intelligence
Volume Number 3
Issue Number 3
e-ISSN 19353820
Language English
Publisher Springer US
Publisher Date 2009-05-27
Publisher Place Boston
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Multi-robot systems Robotic learning Particle swarm optimization 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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