Title:
Human-in-the-loop neural network control of a planetary rover on harsh terrain
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 | |
relation.isAdvisorOfPublication | 6d77e175-105c-4b0b-9548-31f20e60e20a | |
relation.isOrgUnitOfPublication | 5b7adef2-447c-4270-b9fc-846bd76f80f2 | |
relation.isOrgUnitOfPublication | 7c022d60-21d5-497c-b552-95e489a06569 |
Files
Original bundle
1 - 1 of 1
- Name:
- livianu_mathew_j_200812_mast.pdf
- Size:
- 5.49 MB
- Format:
- Adobe Portable Document Format
- Description: