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Risk-Constrained Dynamic Programming for Optimal Mars Entry, Descent, and LandingA chance-constrained dynamic programming algorithm was developed that is capable of making optimal sequential decisions within a user-specified risk bound. This work handles stochastic uncertainties over multiple stages in the CEMAT (Combined EDL-Mobility Analyses Tool) framework. It was demonstrated by a simulation of Mars entry, descent, and landing (EDL) using real landscape data obtained from the Mars Reconnaissance Orbiter. Although standard dynamic programming (DP) provides a general framework for optimal sequential decisionmaking under uncertainty, it typically achieves risk aversion by imposing an arbitrary penalty on failure states. Such a penalty-based approach cannot explicitly bound the probability of mission failure. A key idea behind the new approach is called risk allocation, which decomposes a joint chance constraint into a set of individual chance constraints and distributes risk over them. The joint chance constraint was reformulated into a constraint on an expectation over a sum of an indicator function, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the chance-constraint optimization problem can be turned into an unconstrained optimization over a Lagrangian, which can be solved efficiently using a standard DP approach.
Document ID
20130014121
Acquisition Source
Jet Propulsion Laboratory
Document Type
Other - NASA Tech Brief
External Source(s)
http://www.techbriefs.com/component/content/article/16796
Authors
Ono, Masahiro (California Inst. of Tech. Pasadena, CA, United States)
Kuwata, Yoshiaki (California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
August 27, 2013
Publication Date
July 1, 2013
Publication Information
Publication: NASA Tech Briefs, July 2013
Subject Category
Man/System Technology And Life Support
Report/Patent Number
NPO-48606
Distribution Limits
Public
Copyright
Public Use Permitted.

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