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Ant-Air self-learning algorithm for path planning in a cluttered environment
Ahmad, Rafiq; Plapper, Peter
2014In Applied Mechanics and Materials Journal
Peer reviewed
 

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Keywords :
Path planning; collision detection and avoidance; self-learning algorithm; assembling or disassembling; narrow spaces
Abstract :
[en] Path planning in unstructured area while dealing with narrow spaces is an area of research which is receiving extensive interest. Many existing algorithms are able to produce safe paths but the presented concepts are either not adapted to narrow spaces or they are unable to learn from the past experience to improve repeated movements from the same agent or followed trajectories by other agents. This paper introduces an original concept based on Ant-Air phenomenon for safe path planning in a cluttered environment where narrow passages are treated. The algorithm presented is able to learn from the past experience and hence improve the already generated trajectory further by using some lessons learned from the past experience. The concept is applicable in various domains such as mobile robot path planning, manipulator trajectory generation and part movement in narrow passages in real or virtual assembly/disassembly process.
Disciplines :
Mechanical engineering
Author, co-author :
Ahmad, Rafiq ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
Plapper, Peter ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
Language :
English
Title :
Ant-Air self-learning algorithm for path planning in a cluttered environment
Publication date :
December 2014
Event name :
2nd International Conference on Control, Mechatronics and Automation
Event organizer :
sponsored by International Association of Computer Science and Information Technology
Event place :
Dubai, United Arab Emirates
Event date :
08-12-2014 to 10-12-2014
Audience :
International
Journal title :
Applied Mechanics and Materials Journal
ISSN :
1660-9336
Peer reviewed :
Peer reviewed
Name of the research project :
KIPs
Available on ORBilu :
since 17 September 2014

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