On-Board Evolutionary Algorithm and Off-Line Rule Discovery for Column Formation in Swarm Robotics

Authors:
Asuki Kouno;Jean-Marc Montanier;Shigeru Takano;Nicolas Bredeche;Marc Schoenauer;Michèle Sebag;Einoshin Suzuki
Affiliations:
-;-;-;-;-;-;-
Venue:
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Year:
2011

Citing 11
Cited 1

Principles of data mining

Principles of data mining
Evolution strategies –A comprehensive introduction

Natural Computing: an international journal
Reinforcement Learning in the Multi-Robot Domain

Autonomous Robots
Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics

Proceedings of the Third European Conference on Advances in Artificial Life
Data Mining

Data Mining
Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems

Applied Soft Computing
Evolved swarming without positioning information: an application in aerial communication relay

Autonomous Robots
Evolutionary learning strategy using bug-based search

IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 2
Learning and multiagent reasoning for autonomous agents

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
On-line, on-board evolution of robot controllers

EA'09 Proceedings of the 9th international conference on Artificial evolution
Constructing low-cost swarm robots that march in column formation

ANTS'10 Proceedings of the 7th international conference on Swarm intelligence

Predicting the State of a Person by an Office-Use Autonomous Mobile Robot

WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper aims at building autonomous controllers for swarm robots, specifically aimed at enforcing a given shape formation, here a column formation. The proposed approach features two main characteristics. Firstly, a state-of-the-art evolutionary setting is used to achieve the on-board optimization of the controller, avoiding any simulator-based approach. Secondly, as the cost of physical experiments might be prohibitively high for plain evolutionary approaches, a data mining approach is achieved on the top of evolution, rule discovery is used to discover the most promising regions in the controller search space. The merits of the approach are experimentally validated using a 5 robot formation, showing that the hybrid evolutionary learning process outperforms evolution alone in terms of swarm speed and shape quality.