Autonomous Multiple Teams Establishment for Mobile Sensor Networks by Svms Within a Potential Field

No Thumbnail Available

Date

2012

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier Sci Ltd

Open Access Color

OpenAIRE Downloads

OpenAIRE Views

Research Projects

Organizational Units

Organizational Unit
Department of Mechatronics Engineering
Our purpose in the program is to educate our students for contributing to universal knowledge by doing research on contemporary mechatronics engineering problems and provide them with design, production and publication skills. To reach this goal our post graduate students are offered courses in various areas of mechatronics engineering, encouraged to do research to develop their expertise and their creative side, as well as develop analysis and design skills.

Journal Issue

Events

Abstract

In this work, a new method and algorithm for autonomous teams establishment with mobile sensor network units by SVMs based on task allocations within a potential field is proposed. The sensor network deployed into the environment using the algorithm is composed of robot units with sensing capability of magnetic anomaly of the earth. A new algorithm is developed for task assignment. It is based on the optimization of weights between robots and tasks. The weights are composed of skill ratings of the robots and priorities of the tasks. Multiple teams of mobile units are established in a local area based on these mission vectors. A mission vector is the genetic and gained background information of the mobile units. The genetic background is the inherent structure of their knowledge base in a vector form but it can be dynamically updated with the information gained later on by experience. The mission is performed in a magnetic anomaly environment. The initial values of the mission vectors are loaded by the task assignment algorithm. The mission vectors are updated at the beginning of each sampling period of the motion. Then the teams of robots are created by the support vector machines. A linear optimal hyperplane is calculated by the use of SVM algorithm during training period. Then the robots are classified as teams by use of SVM mechanism embedded in the robots. The support vector machines are implemented in the robots by ordinary op-amps and basic logical gates. Team establishment is tested by simulations and a practical test-bed. Both simulations and the actual operation of the system prove that the system functions satisfactorily. (C) 2012 Elsevier Ltd. All rights reserved.

Description

Keywords

Algorithms, Measurement, Networks, Magnetic field measurement, Robots, Vectors

Turkish CoHE Thesis Center URL

Fields of Science

Citation

WoS Q

Q1

Scopus Q

Source

Volume

45

Issue

5

Start Page

971

End Page

987

Collections