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.

Your Session has timed out. Please sign back in to continue.



Grahic Jump Location
Fig. 1

Model-based prognostics architecture

Grahic Jump Location
Fig. 2

Battery equivalent circuit

Grahic Jump Location
Fig. 3

Rover path for structured driving

Grahic Jump Location
Fig. 5

Most probable point estimation

Grahic Jump Location
Fig. 6

Example power trajectories for unstructured driving

Grahic Jump Location
Fig. 9

ΔkE prediction results using Monte Carlo

Grahic Jump Location
Fig. 10

RDT prediction results using Monte Carlo

Grahic Jump Location
Fig. 11

RDD prediction results using Monte Carlo

Grahic Jump Location
Fig. 12

Sampled power trajectories for structured driving

Grahic Jump Location
Fig. 15

RDT predictions using inverse FORM

Grahic Jump Location
Fig. 16

RDD predictions using inverse FORM




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Articles from Part A: Civil Engineering
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In