A method for generating robot control systems

Bishop, Russell C.

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dc.contributor.author	Bishop, Russell C.
dc.contributor.other	Youngstown State University. Department of Electrical and Computer Engineering.
dc.date.accessioned	2021-10-19T15:35:36Z
dc.date.available	2021-10-19T15:35:36Z
dc.date.issued	2008
dc.identifier.other	277047747
dc.identifier.other	B20382510
dc.identifier.uri	https://jupiter.ysu.edu:443/record=b2038251
dc.identifier.uri	http://hdl.handle.net/1989/16668
dc.description	viii, 105 leaves : ill. ; 29 cm. Thesis (M.S.)--Youngstown State University, 2008. Includes bibliographical references (leaves 104-105).	en_US
dc.description.abstract	This thesis presents a method of generating neural-network based control systems for walking robots. A genetic learning rule is combined with a physics simulation and scoring system in order to find appropriate weights for these networks. This approach produces highly robust neural-network control mechanisms that are capable of handling a wide variety of conditions, such as rough terrain and randomly varying robot proportions. In each of two test runs, the system was able to make the robot walk approximately 1.75 meters (5.8 body lengths) in the physics simulation, over very rough terrain, in 14 seconds of simulation-world time.	en_US
dc.description.sponsorship	Youngstown State University. Department of Electrical and Computer Engineering.	en_US
dc.language.iso	en_US	en_US
dc.relation.ispartofseries	Master's Theses;no. 1121
dc.subject	Robots -- Control systems.	en_US
dc.subject	Mobile robots -- Design and construction.	en_US
dc.subject	Neural networks (Computer science)	en_US
dc.title	A method for generating robot control systems	en_US
dc.type	Thesis	en_US

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