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dc.contributor.authorLivianu, Mathew Josephen_US
dc.date.accessioned2009-01-22T15:47:17Z
dc.date.available2009-01-22T15:47:17Z
dc.date.issued2008-08-25en_US
dc.identifier.urihttp://hdl.handle.net/1853/26576
dc.description.abstractWheel slip is a common problem in planetary rover exploration tasks. During the current Mars Exploration Rover (MER) mission, the Spirit rover almost became trapped on a dune because of wheel slip. As rover missions on harsh terrains expand in scope, mission success will depend not only on rover safety, but also alacrity in task completion. Speed combined with exploration of varied and difficult terrains, the risk of slip increases dramatically. We first characterize slip performance of a rover on harsh terrains by implementing a novel High Fidelity Traversability Analysis (HFTA) algorithm in order to provide slip prediction and detection capabilities to a planetary rover. The algorithm, utilizing path and energy cost functions in conjunction with simulated navigation, allows a rover to select the best path through any given terrain by predicting high slip paths. Integrated software allows the rover to then accurately follow a designated path while compensating for slippage, and reach intended goals independent of the terrain over which it is traversing. The algorithm was verified using ROAMS, a high fidelity simulation package, at 3.5x real time speed. We propose an adaptive path following algorithm as well as a human-trained neural network to traverse multiple harsh terrains using slip as an advantage. On a near-real-time system, and at rover speeds 15 times the current average speed of the Mars Exploration Rovers, we show that the adaptive algorithm traverses paths in less time than a standard path follower. We also train a standard back-propagation neural network, using human and path following data from a near-real-time system. The neural network demonstrates it ability to traverse new paths on multiple terrains and utilize slip to minimize time and path error.en_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectNeural networksen_US
dc.subjectPlanetary roveren_US
dc.subjectSlip controlen_US
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshRoving vehicles (Astronautics)
dc.subject.lcshAutonomous robots
dc.titleHuman-in-the-loop neural network control of a planetary rover on harsh terrainen_US
dc.typeThesisen_US
dc.description.degreeM.S.en_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.advisorCommittee Chair: Dr. Ayanna Howard; Committee Member: Dr. Patricio Vela; Committee Member: Dr. Yoria Wardien_US


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