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Entry Abort Determination Using Non-Adaptive Neural Networks for Mars Precision LandersThe 2009 Mars Science Laboratory (MSL) will attempt the first precision landing on Mars using a modified version of the Apollo Earth entry guidance program. The guidance routine, Entry Terminal Point Controller (ETPC), commands the deployment of a supersonic parachute after converging the range to the landing target. For very dispersed cases, ETPC may not converge the range to the target and safely command parachute deployment within Mach number and dynamic pressure constraints. A full-lift up abort can save 85% of these failed trajectories while abandoning the precision landing objective. Though current MSL requirements do not call for an abort capability, an autonomous abort capability may be desired, for this mission or future Mars precision landers, to make the vehicle more robust. The application of artificial neural networks (NNs) as an abort determination technique was evaluated by personnel at the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC). In order to implement an abort, a failed trajectory needs to be recognized in real time. Abort determination is dependent upon several trajectory parameters whose relationships to vehicle survival are not well understood, and yet the lander must be trained to recognize unsafe situations. Artificial neural networks (NNs) provide a way to model these parameters and can provide MSL with the artificial intelligence necessary to independently declare an abort. Using the 2009 Mars Science Laboratory (MSL) mission as a case study, a non-adaptive NN was designed, trained and tested using Monte Carlo simulations of MSL descent and incorporated into ETPC. Neural network theory, the development history of the MSL NN, and initial testing with severe dust storm entry trajectory cases are discussed in Reference 1 and will not be repeated here. That analysis demonstrated that NNs are capable of recognizing failed descent trajectories and can significantly increase the survivability of MSL for very dispersed cases. NN testing was then broadened to evaluate fully dispersed entry trajectories. The NN correctly classified 99.7% of descent trajectories as abort or nonabort and reduced the probability of an unsafe parachute deployment by 83%. This second, broader testing phase is discussed in this paper.
Document ID
20060056116
Acquisition Source
Johnson Space Center
Document Type
Conference Paper
Authors
Graybeal, Sarah R.
(NASA Johnson Space Center Houston, TX, United States)
Kranzusch, Kara M.
(Iowa State Univ. of Science and Technology Ames, IA, United States)
Date Acquired
August 23, 2013
Publication Date
January 1, 2005
Subject Category
Space Communications, Spacecraft Communications, Command And Tracking
Meeting Information
Meeting: 2005 AIAA Guidance, Navigation and Controls Conference
Location: San Francisco, CA
Country: United States
Start Date: August 15, 2005
End Date: August 18, 2005
Sponsors: American Inst. of Aeronautics and Astronautics
Distribution Limits
Public
Copyright
Other

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