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Research Papers

A Fast Algorithm on Minimum-Time Scheduling of an Autonomous Ground Vehicle Using a Traveling Salesman Framework

[+] Author and Article Information
Soovadeep Bakshi

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

Zeyu Yan

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

Dongmei Chen

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

Qiang Qian

Dalian Auto Tech Dalian,
Liaoning 116051, China
e-mail: qianqiang@dlautotech.com

Yinan Chen

Dalian Auto Tech Dalian,
Liaoning 116051, China
e-mail: chenyinan@dlautotech.com

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received October 9, 2017; final manuscript received June 20, 2018; published online August 1, 2018. Assoc. Editor: Beshah Ayalew.

J. Dyn. Sys., Meas., Control 140(12), 121011 (Aug 01, 2018) (8 pages) Paper No: DS-17-1512; doi: 10.1115/1.4040665 History: Received October 09, 2017; Revised June 20, 2018

Manufacturing automation, especially through implementation of autonomous ground vehicle (AGV) technology, has been under intensive study due to increased productivity and reduced variations. The objective of this paper is to present an algorithm on scheduling of an AGV that traverses desired locations on a manufacturing floor. Although many algorithms have been developed to achieve this objective, most of them rely on exhaustive search, which is time-consuming. A novel two-step algorithm that generates “good,” but not necessarily optimal, solutions for relatively large data sets (≈1000 points) is proposed, taking into account time constraints. A tradeoff analysis of computational expense versus algorithm performance is discussed. The algorithm enables the AGV to find a tour, which is as good as possible within the time constraint, using which it can travel through all given coordinates before returning to the starting location or a specified end point. Compared to exhaustive search methods, this algorithm generates results within a stipulated computation time of 30 s on a laptop personal computer.

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Copyright © 2018 by ASME
Topics: Algorithms
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Figures

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Fig. 1

Yatsenko's solution versus optimal for four points [29]

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Fig. 2

Evolution of Min–Min versus Yatsenko's algorithm

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Fig. 3

Demonstration of cross-canceling in a symmetric Euclidean planar TSP

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Fig. 4

Flowchart for proposed algorithm

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Fig. 5

Optimal solution with 60 points (LP approach on matlab)

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Fig. 6

Min–Min solution with one-pass switching for 60 points

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Fig. 7

Shortest two-end Hamiltonian path solution generated by proposed algorithm

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Fig. 8

Solution for drilling points to be marked on factory floor

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