Path planning of UAV based on hierarchical genetic algorithm with optimized search region

Jinghua Li, Yibin Huang, Zhao Xu, Jing Wang, Mou Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Scopus citations

Abstract

In general, the use of genetic algorithms (GA) for unmanned aerial vehicle (UAV) path planning in the whole mission area will cause detours. To improve this issue, a hierarchical genetic algorithm with optimized search region (OSR-HGA) is proposed. This algorithm reduces the search area of hierarchical genetic algorithm automatically by evaluating the distribution of threat sources in the mission area. To guide the searching direction of the algorithm and reduce the occurrence of detours, the heading correction cost and minimum turning radius cost are added to the cost function. The experimental results show the new method can enhance the stability of path planning algorithm by finding shorter paths with less cost and reducing the occurrence of detours effectively.

Original languageEnglish
Title of host publication2017 13th IEEE International Conference on Control and Automation, ICCA 2017
PublisherIEEE Computer Society
Pages1033-1038
Number of pages6
ISBN (Electronic)9781538626795
DOIs
StatePublished - 4 Aug 2017
Event13th IEEE International Conference on Control and Automation, ICCA 2017 - Ohrid, Macedonia, The Former Yugoslav Republic of
Duration: 3 Jul 20176 Jul 2017

Publication series

NameIEEE International Conference on Control and Automation, ICCA
ISSN (Print)1948-3449
ISSN (Electronic)1948-3457

Conference

Conference13th IEEE International Conference on Control and Automation, ICCA 2017
Country/TerritoryMacedonia, The Former Yugoslav Republic of
CityOhrid
Period3/07/176/07/17

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