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dc.contributor.authorLeedy, Brett Michaelen_US
dc.date.accessioned2014-03-14T20:33:18Z
dc.date.available2014-03-14T20:33:18Z
dc.date.issued2006-03-29en_US
dc.identifier.otheretd-04092006-215052en_US
dc.identifier.urihttp://hdl.handle.net/10919/31666
dc.description.abstractThere are two commonly accepted paradigms for organizing intelligence in robotic vehicles, namely reactive and deliberative. A third, a hybrid paradigm called integrated planning and execution, is considered a combination of the original two. Although these paradigms are well known to researchers, there are few published examples directly comparing their application and performance on similar vehicles operating in identical environments. Virginia Techâ s participation with two nearly identical vehicles in the DARPA Grand Challenge afforded a practical opportunity for such a case study. Both base vehicles were developed by modifying Club Car Pioneer XRT 1500 on-demand four wheel drive base platforms. Cliff was designed to use the reactive paradigm, while Rocky was designed to use the deliberative paradigm. Both vehicles were initially outfitted with sensor suites and computational capabilities commensurate with the paradigm being employed. The author of this thesis coordinated the activities of the two teams of undergraduate and graduate students who implemented the respective designs and software. Both vehicles proved capable of off-road navigation, including road following and obstacle avoidance in complex desert terrain. In the end, however, the reactive paradigm proved to be smoother and more reliable than the deliberative paradigm under the conditions of our testing. While both vehicles were extensively tested and compared using the competing paradigms, the team modified Rocky to use the more effective reactive paradigm for the Grand Challenge events. The deliberative case shows much promise for complex navigation, but added unnecessary complexity to desert road navigation. This case study, while necessarily limited in scope, may help to shed additional light on the tradeoffs and performance of competing approaches to machine intelligence.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartBML_Thesis_3.2.pdfen_US
dc.relation.haspartData_Logs.zipen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectGrand Challengeen_US
dc.subjectReactiveen_US
dc.subjectAutonomous Navigationen_US
dc.subjectDeliberativeen_US
dc.titleTwo Minds for One Vehicle: A Case Study in Deliberative and Reactive Navigationen_US
dc.typeThesisen_US
dc.contributor.departmentMechanical Engineeringen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
dc.contributor.committeechairReinholtz, Charles F.en_US
dc.contributor.committeememberWicks, Alfred L.en_US
dc.contributor.committeememberHong, Dennis W.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-04092006-215052/en_US
dc.date.sdate2006-04-09en_US
dc.date.rdate2006-05-11
dc.date.adate2006-05-11en_US


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