Research Papers

Mission Feasibility Assessment for Mobile Robotic Systems Operating in Stochastic Environments

[+] Author and Article Information
Jonathan R. LeSage

Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712
e-mail: jlesage@utexas.edu

Raul G. Longoria

Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712
e-mail: r.longoria@mail.utexas.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received December 20, 2013; final manuscript received July 7, 2014; published online October 21, 2014. Assoc. Editor: Jongeun Choi.

J. Dyn. Sys., Meas., Control 137(3), 031009 (Oct 21, 2014) (11 pages) Paper No: DS-13-1523; doi: 10.1115/1.4028035 History: Received December 20, 2013; Revised July 07, 2014

This paper presents a hierarchical approach for estimating the mission feasibility, i.e., the probability of mission completion (PoMC), for mobile robotic systems operating in stochastic environments. Mobile robotic systems rely on onboard energy sources that are expended due to stochastic interactions with the environment. Resultantly, a bivariate distribution comprised of energy source (e.g., battery) run-time and mission time marginal distributions can be shown to represent a mission process that characterizes the distribution of all possible missions. Existing methodologies make independent stochastic predictions for battery run-time and mission time. The approach presented makes use of the marginal predictions, as prediction pairs, to allow for Bayesian correlation estimation and improved process characterization. To demonstrate both prediction accuracy and mission classification gains, the proposed methodology is validated using a novel experimental testbed that enables repeated battery discharge studies to be conducted as a small robotic ground vehicle traverses stochastic laboratory terrains.

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Fig. 1

Example bivariate relationship between time required for a mission and the battery run-time

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Fig. 2

Failure regions of mission processes

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Fig. 3

Correlation between marginal distributions critical for estimation of PoMC (a) overestimation of PoMC for discounting correlation and (b) actual PoMC high due to correlation

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Fig. 4

Proposed method for predicting PoMC online during a mobile system mission

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Fig. 5

Results of a Monte Carlo simulation study of a UGV operating in a stochastic environment

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Fig. 6

Modified Thévenin equivalent circuit model of an electrochemical battery system

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Fig. 7

Particle filter forecasting for battery run-time prediction

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Fig. 8

Analytically determining remaining travel time via cumulative velocity forecasting

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Fig. 9

Online correlation estimation scheme utilizing Bayesian inference with marginal predictions of MT and BT

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Fig. 10

Jeffery's uniformative prior for Bayesian inference of correlation with known marginals

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Fig. 11

Single Bayesian estimation of BT and MT correlation via sequential updating using single set of simulation data

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Fig. 12

Mean Bayesian estimation of BT and MT correlation via sequential updating using MC simulation data

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Fig. 13

Differentially driven ground vehicle used for laboratory discharge studies

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Fig. 14

Schematic depiction of the automated stochastic terrain test stand

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Fig. 15

Experimental results of 22 discharge tests with a bivariate normal confidence interval

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Fig. 16

Cumulative relative accuracy analysis of battery run-time prediction

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Fig. 17

Comparison of varied mission profiles on the experimental data

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Fig. 18

Probability of mission completion predictions for experiment one for different MDs

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Fig. 19

Cumulative relative accuracy comparison between including mission process correlation for differing missions

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Fig. 20

Receiver operation curve for mission classification assessment




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