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 BookConference contributionpeer-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 languageEnglish
Title of host publication2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings
EditorsKeeley Crockett, Sanaz Mostaghim, Alice Smith, Carlos A. Coello Coello, Piero Bonissone, Dipti Srinivasan, Anna Wilbik
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-8
Number of pages8
ISBN (Electronic)9781728190488
ISBN (Print)9781728190495
DOIs
Publication statusPublished - Dec 2021
Externally publishedYes
Event2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Orlando, United States
Duration: 5 Dec 20217 Dec 2021

Publication series

Name2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings

Conference

Conference2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021
Country/TerritoryUnited States
CityOrlando
Period5/12/217/12/21

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