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Distributed Particle Swarm Optimization for limited-time adaptation with real robots

Published online by Cambridge University Press:  22 November 2013

Ezequiel Di Mario and
Alcherio Martinoli
Ezequiel Di Mario*
Affiliation:
Distributed Intelligent Systems and Algorithms Laboratory, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
Alcherio Martinoli
Affiliation:
Distributed Intelligent Systems and Algorithms Laboratory, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
*
*Corresponding author. E-mail: ezequiel.dimario@epfl.ch
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Summary

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Evaluative techniques offer a tremendous potential for online controller design. However, when the optimization space is large and the performance metric is noisy, the overall adaptation process becomes extremely time consuming. Distributing the adaptation process reduces the required time and increases robustness to failure of individual agents. In this paper, we analyze the role of the four algorithmic parameters that determine the total evaluation time in a distributed implementation of a Particle Swarm Optimization (PSO) algorithm. For an obstacle avoidance case study using up to eight robots, we explore in simulation the lower boundaries of these parameters and propose a set of empirical guidelines for choosing their values. We then apply these guidelines to a real robot implementation and show that it is feasible to optimize 24 control parameters per robot within 2 h, a limited amount of time determined by the robots' battery life. We also show that a hybrid simulate-and-transfer approach coupled with a noise-resistant PSO algorithm can be used to further reduce experimental time as compared to a pure real-robot implementation.

Keywords

DARS2012Multi-robot systemsMobile robotsParticle Swarm OptimizationDistributed learning

Information

Type
Articles
Information
Robotica , Volume 32 , Issue 2: Distributed Autonomous Robotic Systems (DARS). , March 2014, pp. 193 - 208
Copyright
Copyright © Cambridge University Press 2013 

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