Human-in-the-loop neural network control of a planetary rover on harsh terrain

dc.contributor.advisor Howard, Ayanna M.
dc.contributor.author Livianu, Mathew Joseph en_US
dc.contributor.committeeMember Patricio Vela
dc.contributor.committeeMember Yoria Wardi
dc.contributor.department Electrical and Computer Engineering en_US
dc.date.accessioned 2009-01-22T15:47:17Z
dc.date.available 2009-01-22T15:47:17Z
dc.date.issued 2008-08-25 en_US
dc.description.abstract Wheel 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.description.degree M.S. en_US
dc.identifier.uri http://hdl.handle.net/1853/26576
dc.publisher Georgia Institute of Technology en_US
dc.subject Neural networks en_US
dc.subject Planetary rover en_US
dc.subject Slip control en_US
dc.subject.lcsh Neural networks (Computer science)
dc.subject.lcsh Roving vehicles (Astronautics)
dc.subject.lcsh Autonomous robots
dc.title Human-in-the-loop neural network control of a planetary rover on harsh terrain en_US
dc.type Text
dc.type.genre Thesis
dspace.entity.type Publication
local.contributor.advisor Howard, Ayanna M.
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
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relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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