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A Cognitive Approach to Mobile Robot Environment Mapping and Path Planning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	A Cognitive Approach to Mobile Robot Environment Mapping and Path Planning./
Author:	Zeno, Peter J.
Description:	1 online resource (107 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Subject:	Robotics. -
Online resource:	click for full text (PQDT)
ISBN:	9780355117660

A Cognitive Approach to Mobile Robot Environment Mapping and Path Planning.
Zeno, Peter J.

A Cognitive Approach to Mobile Robot Environment Mapping and Path Planning. - 1 online resource (107 pages)

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.

Thesis (Ph.D.)--University of Bridgeport, 2017.

Includes bibliographical references

This thesis presents a novel neurophysiological based navigation system which uses less memory and power than other neurophysiological based systems, as well as traditional navigation systems performing similar tasks. This is accomplished by emulating the rodent's specialized navigation and spatial awareness brain cells, as found in and around the hippocampus and entorhinal cortex, at a higher level of abstraction than previously used neural representations. Specifically, the focus of this research will be on replicating place cells, boundary cells, head direction cells, and grid cells using data structures and logic driven by each cell's interpreted behavior. This method is used along with a unique multimodal source model for place cell activation to create a cognitive map. Path planning is performed by using a combination of Euclidean distance path checking, goal memory, and the A* algorithm. Localization is accomplished using simple, low power sensors, such as a camera, ultrasonic sensors, motor encoders and a gyroscope. The place code data structures are initialized as the mobile robot finds goal locations and other unique locations, and are then linked as paths between goal locations, as goals are found during exploration. The place code creates a hybrid cognitive map of metric and topological data. In doing so, much less memory is needed to represent the robot's roaming environment, as compared to traditional mapping methods, such as occupancy grids. A comparison of the memory and processing savings are presented, as well as to the functional similarities of our design to the rodent' specialized navigation cells.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355117660Subjects--Topical Terms:

561941
Robotics.
Index Terms--Genre/Form:

554714
Electronic books.

A Cognitive Approach to Mobile Robot Environment Mapping and Path Planning.
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040 $a MiAaPQ $b eng $c MiAaPQ
100 1 $a Zeno, Peter J. $3 1186989
245 1 2 $a A Cognitive Approach to Mobile Robot Environment Mapping and Path Planning.
264 0 $c 2017
300 $a 1 online resource (107 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
500 $a Advisers: Tarek M. Sobh; Sarosh Patel.
502 $a Thesis (Ph.D.)--University of Bridgeport, 2017.
504 $a Includes bibliographical references
520 $a This thesis presents a novel neurophysiological based navigation system which uses less memory and power than other neurophysiological based systems, as well as traditional navigation systems performing similar tasks. This is accomplished by emulating the rodent's specialized navigation and spatial awareness brain cells, as found in and around the hippocampus and entorhinal cortex, at a higher level of abstraction than previously used neural representations. Specifically, the focus of this research will be on replicating place cells, boundary cells, head direction cells, and grid cells using data structures and logic driven by each cell's interpreted behavior. This method is used along with a unique multimodal source model for place cell activation to create a cognitive map. Path planning is performed by using a combination of Euclidean distance path checking, goal memory, and the A* algorithm. Localization is accomplished using simple, low power sensors, such as a camera, ultrasonic sensors, motor encoders and a gyroscope. The place code data structures are initialized as the mobile robot finds goal locations and other unique locations, and are then linked as paths between goal locations, as goals are found during exploration. The place code creates a hybrid cognitive map of metric and topological data. In doing so, much less memory is needed to represent the robot's roaming environment, as compared to traditional mapping methods, such as occupancy grids. A comparison of the memory and processing savings are presented, as well as to the functional similarities of our design to the rodent' specialized navigation cells.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Robotics. $3 561941
650 4 $a Neurosciences. $3 593561
655 7 $a Electronic books. $2 local $3 554714
690 $a 0771
690 $a 0317
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Bridgeport. $b Computer Science and Engineering. $3 1183815
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10604778 $z click for full text (PQDT)