Research Papers

Development of Proportional–Integral–Derivative and Fuzzy Control Strategies for Navigation in Agricultural Environments

[+] Author and Article Information
Stephanie Bonadies, Neal Smith

Department of Mechanical Engineering,
University of Maryland,
Baltimore County,
Baltimore, MD 21250

Nathan Niewoehner

Department of Computer Science
and Electrical Engineering,
University of Maryland,
Baltimore County,
Baltimore, MD 21250

Andrew S. Lee

School of Engineering,
The University of Guelph,
Guelph, ON N1G 2W1, Canada

Alan M. Lefcourt

Environmental Microbial
and Food Safety Laboratory,
The United States Department of Agriculture,
Beltsville, MD 20705

S. Andrew Gadsden

School of Engineering,
The University of Guelph,
Guelph, ON N1G 2W1, Canada
e-mail: gadsden@uoguelph.ca

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received September 30, 2016; final manuscript received November 7, 2017; published online December 22, 2017. Editor: Joseph Beaman. This work is in part a work of the U.S. Government. ASME disclaims all interest in the U.S. Government's contributions.

J. Dyn. Sys., Meas., Control 140(6), 061007 (Dec 22, 2017) (6 pages) Paper No: DS-16-1473; doi: 10.1115/1.4038504 History: Received September 30, 2016; Revised November 07, 2017

Farming and agriculture is an area that may benefit from improved use of automation in order to increase working hours and improve food quality and safety. In this paper, a commercial robot was purchased and modified, and crop row navigational software was developed to allow the ground-based robot to autonomously navigate a crop row setting. A proportional–integral–derivative (PID) controller and a fuzzy logic controller were developed to compare the efficacy of each controller based on which controller navigated the crop row more reliably. Results of the testing indicate that both controllers perform well, with some differences depending on the scenario.

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Fig. 1

Illustration of row center determination [9]

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Fig. 2

Four-connectedness (left) verses eight-connectedness (right) [10]

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Fig. 3

Controller lane detection flowchart

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Fig. 4

Jaguar 4 × 4 platform configuration

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Fig. 5

Indoor crop lane simulation testing environment

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Fig. 6

PID indoor test trial 1 error (left offset)

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Fig. 7

PID indoor test trial 11 error (right offset)

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Fig. 8

PID indoor test trial 30 error (middle)

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Fig. 9

Fuzzy logic indoor test trial 19 error (left offset)

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Fig. 10

Fuzzy logic indoor test trial 2 error (right offset)

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Fig. 11

Fuzzy logic indoor test trial 12 error (middle)



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