NDLI: Behavior learning and evolution of swarm robot system using SVM

Content Provider	IEEE Xplore Digital Library
Author	Sang-Wook Seo Kwang-Eun Ko Hyun-Chang Yang Kwee-Bo Sim
Copyright Year	2007
Description	Author affiliation: Chung-Ang Univ., Seoul (Sang-Wook Seo; Kwang-Eun Ko; Hyun-Chang Yang; Kwee-Bo Sim)
Abstract	In swarm robot systems, each robot must behaves by itself according to the its states and environments, and if necessary, must cooperates with other robots in order to carry out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, reinforcement learning method with SVM based on structural risk minimization and distributed genetic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. By distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning that basis of SVM is adopted in this paper.
Starting Page	1238
Ending Page	1242
File Size	910549
Page Count	5
File Format	PDF
ISBN	9788995003862
DOI	10.1109/ICCAS.2007.4406524
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-17
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	ICASE
Subject Keyword	Mobile communication SVM Mobile robots Behavior Learning Robotics and automation Genetic algorithms Orbital robotics Support vector machines Learning Robot kinematics Swarm Robot Support vector machine classification Distributed Genetic Algorithm Automatic control Evolution
Content Type	Text
Resource Type	Article

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