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dc.contributor.advisor Cheatham, John B., Jr.
dc.creatorNorwood, John David
dc.date.accessioned 2009-06-04T00:13:51Z
dc.date.available 2009-06-04T00:13:51Z
dc.date.issued 1989
dc.identifier.urihttps://hdl.handle.net/1911/13457
dc.description.abstract Robotic path planning and obstacle avoidance has been the subject of intensive research in recent years. Most solutions to this problem have been reached through the use of traditional Artificial Intelligence search techniques. However, these methods have proven inadequate when applied to highly unstructured or unknown environments. By using an Artificial Neural Network, one can generate near optimal paths using only low level information about the scene. In this way, it is possible to navigate from a start position to a goal position while avoiding all obstacles. Major advantages of the method presented herein are that the solution is very fast and does not rely on any a priori knowledge of the robot's environment. The system presented herein has proven very effective for path generation when used in conjunction with a simulated Laser Imaging System.
dc.format.extent 106 p.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectMechanical engineering
Computer science
Artificial intelligence
dc.title Robotic path planning and obstacle avoidance: A neural network approach
dc.type.genre Thesis
dc.type.material Text
thesis.degree.department Computer Science
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Masters
thesis.degree.name Master of Science
dc.identifier.citation Norwood, John David. "Robotic path planning and obstacle avoidance: A neural network approach." (1989) Master’s Thesis, Rice University. https://hdl.handle.net/1911/13457.


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