UGV Path Planning based on an Improved Continuous Ant Colony Optimisation Algorithm

Jing Liu; Aya Hussein; Sreenatha Anavatti; Matthew Garratt; Hussein A. Abbass

doi:10.1109/SSCI50451.2021.9659847

UGV Path Planning based on an Improved Continuous Ant Colony Optimisation Algorithm

Jing Liu, Aya Hussein, Sreenatha Anavatti, Matthew Garratt, Hussein A. Abbass

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Path planning has always been an essential component of autonomy for Unmanned Ground Vehicles. In this paper, we present an improved continuous ant colony optimisation algorithm with differential evolution operator and local search, namely LIACODER, to solve the path planning problem with improved accuracy. A transformed coordinate system is introduced, based on which a solution repair method is presented to accelerate the convergence speed and facilitate the search process for the most feasible and optimal path. Experiments are conducted in both abstract and physical environments to compare LIACODERto classical path planning algorithms such as A*, D∗ and other state-of-the-art swarm optimisation algorithms. The superior performance of LIACODERin terms of finding feasible path with lower cost are validated.

Original language	English
Title of host publication	2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings
Editors	Keeley Crockett, Sanaz Mostaghim, Alice Smith, Carlos A. Coello Coello, Piero Bonissone, Dipti Srinivasan, Anna Wilbik
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1-8
Number of pages	8
ISBN (Electronic)	9781728190488
ISBN (Print)	9781728190495
DOIs	https://doi.org/10.1109/SSCI50451.2021.9659847
Publication status	Published - Dec 2021
Externally published	Yes
Event	2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Orlando, United States Duration: 5 Dec 2021 → 7 Dec 2021

Publication series

Name	2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings

Conference

Conference	2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021
Country/Territory	United States
City	Orlando
Period	5/12/21 → 7/12/21

Access to Document

10.1109/SSCI50451.2021.9659847

Cite this

Liu, J., Hussein, A., Anavatti, S., Garratt, M., & Abbass, H. A. (2021). UGV Path Planning based on an Improved Continuous Ant Colony Optimisation Algorithm. In K. Crockett, S. Mostaghim, A. Smith, C. A. Coello Coello, P. Bonissone, D. Srinivasan, & A. Wilbik (Eds.), 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings (pp. 1-8). (2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/SSCI50451.2021.9659847

Liu, Jing ; Hussein, Aya ; Anavatti, Sreenatha et al. / UGV Path Planning based on an Improved Continuous Ant Colony Optimisation Algorithm. 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings. editor / Keeley Crockett ; Sanaz Mostaghim ; Alice Smith ; Carlos A. Coello Coello ; Piero Bonissone ; Dipti Srinivasan ; Anna Wilbik. IEEE, Institute of Electrical and Electronics Engineers, 2021. pp. 1-8 (2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings).

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title = "UGV Path Planning based on an Improved Continuous Ant Colony Optimisation Algorithm",

abstract = "Path planning has always been an essential component of autonomy for Unmanned Ground Vehicles. In this paper, we present an improved continuous ant colony optimisation algorithm with differential evolution operator and local search, namely LIACODER, to solve the path planning problem with improved accuracy. A transformed coordinate system is introduced, based on which a solution repair method is presented to accelerate the convergence speed and facilitate the search process for the most feasible and optimal path. Experiments are conducted in both abstract and physical environments to compare LIACODERto classical path planning algorithms such as A*, D∗ and other state-of-the-art swarm optimisation algorithms. The superior performance of LIACODERin terms of finding feasible path with lower cost are validated.",

keywords = "A, Continuous ant colony optimisation, Real robot, UGV path planning",

author = "Jing Liu and Aya Hussein and Sreenatha Anavatti and Matthew Garratt and Abbass, {Hussein A.}",

note = "Funding Information: ACKNOWLEDGEMENT This work is partially supported by China Scholarship Council (Grant No. 201708440307), UNSW-Canberra scholarship program, and the Australian Research Council Grant DP200101211. Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 ; Conference date: 05-12-2021 Through 07-12-2021",

year = "2021",

month = dec,

doi = "10.1109/SSCI50451.2021.9659847",

language = "English",

isbn = "9781728190495",

series = "2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

pages = "1--8",

editor = "Crockett, {Keeley } and Mostaghim, {Sanaz } and Smith, {Alice } and {Coello Coello}, { Carlos A. } and { Bonissone}, Piero and Srinivasan, {Dipti } and Wilbik, { Anna }",

booktitle = "2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings",

address = "United States",

}

Liu, J, Hussein, A, Anavatti, S, Garratt, M & Abbass, HA 2021, UGV Path Planning based on an Improved Continuous Ant Colony Optimisation Algorithm. in K Crockett, S Mostaghim, A Smith, CA Coello Coello, P Bonissone, D Srinivasan & A Wilbik (eds), 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings. 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings, IEEE, Institute of Electrical and Electronics Engineers, pp. 1-8, 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021, Orlando, United States, 5/12/21. https://doi.org/10.1109/SSCI50451.2021.9659847

UGV Path Planning based on an Improved Continuous Ant Colony Optimisation Algorithm. / Liu, Jing; Hussein, Aya; Anavatti, Sreenatha et al.
2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings. ed. / Keeley Crockett; Sanaz Mostaghim; Alice Smith; Carlos A. Coello Coello; Piero Bonissone; Dipti Srinivasan; Anna Wilbik. IEEE, Institute of Electrical and Electronics Engineers, 2021. p. 1-8 (2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - UGV Path Planning based on an Improved Continuous Ant Colony Optimisation Algorithm

AU - Liu, Jing

AU - Hussein, Aya

AU - Anavatti, Sreenatha

AU - Garratt, Matthew

AU - Abbass, Hussein A.

N1 - Funding Information: ACKNOWLEDGEMENT This work is partially supported by China Scholarship Council (Grant No. 201708440307), UNSW-Canberra scholarship program, and the Australian Research Council Grant DP200101211. Publisher Copyright: © 2021 IEEE.

PY - 2021/12

Y1 - 2021/12

N2 - Path planning has always been an essential component of autonomy for Unmanned Ground Vehicles. In this paper, we present an improved continuous ant colony optimisation algorithm with differential evolution operator and local search, namely LIACODER, to solve the path planning problem with improved accuracy. A transformed coordinate system is introduced, based on which a solution repair method is presented to accelerate the convergence speed and facilitate the search process for the most feasible and optimal path. Experiments are conducted in both abstract and physical environments to compare LIACODERto classical path planning algorithms such as A*, D∗ and other state-of-the-art swarm optimisation algorithms. The superior performance of LIACODERin terms of finding feasible path with lower cost are validated.

AB - Path planning has always been an essential component of autonomy for Unmanned Ground Vehicles. In this paper, we present an improved continuous ant colony optimisation algorithm with differential evolution operator and local search, namely LIACODER, to solve the path planning problem with improved accuracy. A transformed coordinate system is introduced, based on which a solution repair method is presented to accelerate the convergence speed and facilitate the search process for the most feasible and optimal path. Experiments are conducted in both abstract and physical environments to compare LIACODERto classical path planning algorithms such as A*, D∗ and other state-of-the-art swarm optimisation algorithms. The superior performance of LIACODERin terms of finding feasible path with lower cost are validated.

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M3 - Conference contribution

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BT - 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings

A2 - Crockett, Keeley

A2 - Mostaghim, Sanaz

A2 - Smith, Alice

A2 - Coello Coello, Carlos A.

A2 - Bonissone, Piero

A2 - Srinivasan, Dipti

A2 - Wilbik, Anna

PB - IEEE, Institute of Electrical and Electronics Engineers

T2 - 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021

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ER -

Liu J, Hussein A, Anavatti S, Garratt M, Abbass HA. UGV Path Planning based on an Improved Continuous Ant Colony Optimisation Algorithm. In Crockett K, Mostaghim S, Smith A, Coello Coello CA, Bonissone P, Srinivasan D, Wilbik A, editors, 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings. IEEE, Institute of Electrical and Electronics Engineers. 2021. p. 1-8. (2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings). doi: 10.1109/SSCI50451.2021.9659847

UGV Path Planning based on an Improved Continuous Ant Colony Optimisation Algorithm

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