Parallel niche genetic algorithm for UAV fleet stealth coverage 3D corridors real-time planning

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

Abstract

This paper presents a parallel improved niche genetic algorithm (PINGA) for 3D stealth coverage corridors real-time planning of unmanned aerial vehicles (UAVs) operating in a threat rich environment. 3D corridor was suggested to meet the diversity kinematics constraints of UAVs. Niche genetic algorithm (NGA) was improved by merging neighborhood mutation operator and hill climbing algorithm, and performed in parallel. Additionally, the crowding strategy based on high value targets was used to generate coverage trajectories in the area of interest (AOI). Preliminary results in virtual environments show that the approach for UAVs high quality flight corridors planning is real-time and effective.

Original languageEnglish
Title of host publicationAdvances in Mechatronics, Automation and Applied Information Technologies
Pages1189-1196
Number of pages8
DOIs
StatePublished - 2014
Event2013 International Conference on Mechatronics and Semiconductor Materials, ICMSCM 2013 - Xi'an, China
Duration: 28 Sep 201329 Sep 2013

Publication series

NameAdvanced Materials Research
Volume846-847
ISSN (Print)1022-6680

Conference

Conference2013 International Conference on Mechatronics and Semiconductor Materials, ICMSCM 2013
Country/TerritoryChina
CityXi'an
Period28/09/1329/09/13

Keywords

  • Genetic algorithms
  • Parallel computing
  • Path planning
  • UAV

Fingerprint

Dive into the research topics of 'Parallel niche genetic algorithm for UAV fleet stealth coverage 3D corridors real-time planning'. Together they form a unique fingerprint.

Cite this