Autonomous Navigation of Mobile Robots

Dip Narayan Ray

ISBN 10: 3659198080 / ISBN 13: 9783659198083

Published by LAP LAMBERT Academic Publishing Aug 2012, 2012

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -Mobile robots are being popular due to their extensive application in different hazardous/ unapproachable areas, such as outer space, underwater explorations, underground coal mines monitoring, inspection in chemical/toxic/ nuclear factories etc. The conventional/ classical robotics may not serve the purpose well if these environments are totally unknown/unpredictable (even the programmer cannot imagine it). In such cases robot learning may be the best option. Learning from the past experiences, is one such way for real time application of robots for completely unknown environments. Reinforcement learning is one of the best learning methods for robots using a constant system-environment interaction. Both single and multi-agent concepts are available for implementation of learning. The current research work describes a multi-agent based reinforcement learning using the concept of behaviour-based robotics for autonomous exploration of mobile robots.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 272 pp. Englisch.

Seller Inventory # 9783659198083

Bibliographic Details

• Title: Autonomous Navigation of Mobile Robots
• Publisher: LAP LAMBERT Academic Publishing Aug 2012
• Publication Date: 2012
• Language: English
• ISBN 10: 3659198080
• ISBN 13: 9783659198083
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 17D millimeters 220 millimeters width by 150 millimeters height by 17 millimeters depth
• Weight: 423 grams

About this title

Synopsis

Mobile robots are being popular due to their extensive application in different hazardous/ unapproachable areas, such as outer space, underwater explorations, underground coal mines monitoring, inspection in chemical/toxic/ nuclear factories etc. The conventional/ classical robotics may not serve the purpose well if these environments are totally unknown/unpredictable (even the programmer cannot imagine it). In such cases robot learning may be the best option. Learning from the past experiences, is one such way for real time application of robots for completely unknown environments. Reinforcement learning is one of the best learning methods for robots using a constant system-environment interaction. Both single and multi-agent concepts are available for implementation of learning. The current research work describes a multi-agent based reinforcement learning using the concept of behaviour-based robotics for autonomous exploration of mobile robots.

About the Author

Dip Narayan Ray is working in the field of Behaviour based robotics, Machine Learning (especially Reinforcement Learning) at CSIR – CMERI, Durgapur. Dr. Ray has obtained his Bachelor degree in Mechanical Engineering from NIT, Durgapur and completed his PhD in Mobile Robot navigation fusing Behaviour-based Robotics and Reinforcement Learning.