Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

New algorithm for behaviour-based mobile robot navigation in cluttered environment using neural network architecture

New algorithm for behaviour-based mobile robot navigation in cluttered environment using neural... PurposeThis study concerns an on-line path planning technique for a behaviour-based wheeled mobile robot local navigation in an unknown environment with hurdles, using the feedforward back-propagation neural network sensor-actuator control technique. The purpose of this study is to find the non-collision path for the mobile robot moving towards the goal in a cluttered environment.Design/methodology/approachNeural network architecture input layers are the different hurdle distance information, which are acquired by an array of equipped sensors, and the output layer is the turning angle (motor control). In this way, the mobile robot is effectively being trained to move autonomously in the environment.FindingsComputer simulation and real-time experimental results show that the proposed neural network controller can improve navigation performance in cluttered and unknown environments.Originality/valueThe proposed neural network controller gives better results (in terms of path length) as compared to previously developed models, which verifies the effectiveness of the proposed architecture. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png World Journal of Engineering Emerald Publishing

New algorithm for behaviour-based mobile robot navigation in cluttered environment using neural network architecture

World Journal of Engineering , Volume 13 (2): 13 – Apr 8, 2016

Loading next page...
 
/lp/emerald-publishing/new-algorithm-for-behaviour-based-mobile-robot-navigation-in-cluttered-cK00yg7G4S

References (16)

Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1708-5284
DOI
10.1108/WJE-04-2016-018
Publisher site
See Article on Publisher Site

Abstract

PurposeThis study concerns an on-line path planning technique for a behaviour-based wheeled mobile robot local navigation in an unknown environment with hurdles, using the feedforward back-propagation neural network sensor-actuator control technique. The purpose of this study is to find the non-collision path for the mobile robot moving towards the goal in a cluttered environment.Design/methodology/approachNeural network architecture input layers are the different hurdle distance information, which are acquired by an array of equipped sensors, and the output layer is the turning angle (motor control). In this way, the mobile robot is effectively being trained to move autonomously in the environment.FindingsComputer simulation and real-time experimental results show that the proposed neural network controller can improve navigation performance in cluttered and unknown environments.Originality/valueThe proposed neural network controller gives better results (in terms of path length) as compared to previously developed models, which verifies the effectiveness of the proposed architecture.

Journal

World Journal of EngineeringEmerald Publishing

Published: Apr 8, 2016

There are no references for this article.