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Research Papers

Predicting Remaining Driving Time and Distance of a Planetary Rover Under Uncertainty

[+] Author and Article Information
Matthew Daigle

NASA Ames Research Center,
Moffett Field, CA 94035
e-mail: matthew.j.daigle@nasa.gov

Shankar Sankararaman

NASA Ames Research Center (SGT Inc.),
Moffett Field, CA 94035
e-mail: shankar.sankararaman@nasa.gov

1Corresponding author.

Manuscript received June 22, 2015; final manuscript received February 19, 2016; published online August 19, 2016. Assoc. Editor: Sankaran Mahadevan.This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.

ASME J. Risk Uncertainty Part B 2(4), 041001 (Aug 19, 2016) (11 pages) Paper No: RISK-15-1080; doi: 10.1115/1.4032848 History: Received June 22, 2015; Accepted February 19, 2016

The operations of a planetary rover depend critically upon the amount of power that can be delivered by its batteries. In order to plan the future operation, it is important to make reliable predictions regarding the end-of-discharge (EOD) time, which can be used to estimate the remaining driving time (RDT) and remaining driving distance (RDD). These quantities are stochastic in nature, not only because there are several sources of uncertainty that affect the rover’s operation but also since the future operating conditions cannot be known precisely. This paper presents a computational methodology to predict these stochastic quantities, based on a model of the rover and its batteries. We utilize a model-based prognostics framework that characterizes and incorporates the various sources of uncertainty into these predictions, thereby assisting operational decision-making. We consider two different types of driving scenarios and develop methods for each to characterize the associated uncertainty. Monte Carlo sampling and the inverse first-order reliability method are used to compute the stochastic predictions of EOD time, RDT, and RDD.

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References

Figures

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Fig. 1

Model-based prognostics architecture

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Fig. 2

Battery equivalent circuit

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Fig. 3

Rover path for structured driving

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Fig. 5

Most probable point estimation

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Fig. 6

Example power trajectories for unstructured driving

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Fig. 9

ΔkE prediction results using Monte Carlo

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Fig. 10

RDT prediction results using Monte Carlo

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Fig. 11

RDD prediction results using Monte Carlo

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Fig. 12

Sampled power trajectories for structured driving

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Fig. 15

RDT predictions using inverse FORM

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Fig. 16

RDD predictions using inverse FORM

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