Technical Briefs

Specialized Algorithm for Navigation of a Micro Hopping Air Vehicle Using Only Inertial Sensors

[+] Author and Article Information
Edward Scheuermann

Graduate Research Assistant
School of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: escheuermann3@gatech.edu

Mark Costello

School of Aerospace Engineering,
School of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: mark.costello@aerospace.gatech.edu

Contributed by the Dynamic Systems Division of ASME for publication in the Journal of Dynamic Systems, Measurement, and Control. Manuscript received October 14, 2011; final manuscript received August 24, 2012; published online December 21, 2012. Assoc. Editor: Won-jong Kim.

J. Dyn. Sys., Meas., Control 135(2), 024505 (Dec 21, 2012) (8 pages) Paper No: DS-11-1315; doi: 10.1115/1.4007975 History: Received October 14, 2011; Revised August 24, 2012

The need for accurate and reliable navigation techniques for micro air vehicles plays an important part in enabling autonomous operation. Traditional navigation systems typically rely on periodic global positioning system updates and provide little benefit when operating indoors or in other similarly shielded environments. Moreover, direct integration of the onboard inertial measurement unit data stream often results in substantial drift errors yielding virtually unusable positional information. This paper presents a new strategy for obtaining an accurate navigation solution for the special case of a micro hopping air vehicle, beginning from some known location and heading, using only one triaxial accelerometer and one triaxial gyroscope. Utilizing the unique dynamics of the hopping vehicle, a piece-wise navigation solution is constructed by selectively integrating the inertial data stream for only those short periods of time while the vehicle is airborne. Interhop data post processing and sensor bias recalibration are also used to further improve estimation accuracy. To assess the performance of the proposed algorithm, a series of tests were conducted in which the estimated vehicle position following a sequence of 10 consecutive hops was compared with measurements from an optical motion-capture system. On average, the final estimated vehicle position was within 0.70 m or just over 6% from its actual location based on a total traveled distance of approximately 11 m.

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Fig. 1

Micro hopping rotochute schematic diagram

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Fig. 2

AVR RZUSBstick base station (left) with GINA wireless IMU (right)

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Fig. 3

Navigation algorithm flowchart

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Fig. 4

Micro hopping rotochute prototype vehicle

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Fig. 5

Indoor flight facility with optical motion capture system [11]

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Fig. 6

Altitude versus cross range versus range for example hop

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Fig. 7

Cross range versus range for example hop

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Fig. 8

Roll and pitch angle versus time for example hop

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Fig. 9

Heading (yaw) angle versus time for example hop

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Fig. 10

Cross range versus range for example 10 hop sequence



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