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doctoral thesis

Distributed Multi-Robot Learning using Particle Swarm Optimization

Di Mario, Ezequiel Leonardo  
2015

This thesis studies the automatic design and optimization of high-performing robust controllers for mobile robots using exclusively on-board resources. Due to the often large parameter space and noisy performance metrics, this constitutes an expensive optimization problem. Population-based learning techniques have been proven to be effective in dealing with noise and are thus promising tools to approach this problem. We focus this research on the Particle Swarm Optimization (PSO) algorithm, which, in addition to dealing with noise, allows a distributed implementation, speeding up the optimization process and adding robustness to failure of individual agents. In this thesis, we systematically analyze the different variables that affect the learning process for a multi-robot obstacle avoidance benchmark. These variables include algorithmic parameters, controller architecture, and learning and testing environments. The analysis is performed on experimental setups of increasing evaluation time and complexity: numerical benchmark functions, high-fidelity simulations, and experiments with real robots. Based on this analysis, we apply the distributed PSO framework to learn a more complex, collaborative task: flocking. This attempt to learn a collaborative task in a distributed manner on a large parameter space is, to our knowledge, the first of such kind. In addition, we address the problem of noisy performance evaluations encountered in these robotic tasks and present a %new distributed PSO algorithm for dealing with noise suitable for resource-constrained mobile robots due to its low requirements in terms of memory and limited local communication.

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Type
doctoral thesis
DOI
10.5075/epfl-thesis-6707
Author(s)
Di Mario, Ezequiel Leonardo  
Advisors
Martinoli, Alcherio  
Jury

Prof. Colin Neil Jones (président) ; Prof. Alcherio Martinoli (directeur de thèse) ; Prof. Auke Ijspeert, Prof. Luca Gambardella, Dr Roderich Gross (rapporteurs)

Date Issued

2015

Publisher

EPFL

Publisher place

Lausanne

Public defense year

2015-08-21

Thesis number

6707

Total of pages

167

Subjects

Distributed Learning

•

Multi-Robot Systems

•

Particle Swarm Optimization

EPFL units
DISAL  
Faculty
ENAC  
School
IIE  
Doctoral School
EDEE  
Available on Infoscience
August 18, 2015
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/117105
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