A Flexible Genetic Algorithm System for Multi-UAV Surveillance: Algorithm and Flight Testing-Reference-Cited by-同舟云学术

A Flexible Genetic Algorithm System for Multi-UAV Surveillance: Algorithm and Flight Testing

Published:2015-01 Issue:01 Volume:03 Page:49-62
ISSN:2301-3850
Container-title:Unmanned Systems
language:en
Short-container-title:Un. Sys.

Author:

Darrah Marjorie¹,Wilhelm Jay²,Munasinghe Thilanka¹,Duling Kristin¹,Yokum Steve³,Sorton Eric³,Rojas Jonathan²,Wathen Mitchell⁴

Affiliation:

1. Mathematics Department, West Virginia University, P. O. Box 6310, Morgantown, West Virginia, WV 26506, USA

2. Department of Aerospace Engineering, West Virginia University, Morgantown, West Virginia, WV 26506, USA

3. West Virginia High Technology Consortium Foundation, 1000 Technology Drive, Fairmont, West Virginia, WV 26554, USA

4. Army Research Laboratory, 2800 Powder Mill Rd, Adelphi, MD 20783, USA

Abstract

This paper discusses the development and testing of a flexible genetic algorithm (GA)-based system used for tasking a team of unmanned aerial vehicles (UAVs) to complete a coordinated surveillance mission. The GA development, laboratory testing of the GA to ensure convergence to a "good" solution, integration testing with two ground stations, and the field testing of the algorithms are explained. The algorithm was found to be robust and flexible enough to work in various settings with different UAV types and ground stations.

Publisher

World Scientific Pub Co Pte Lt

Subject

Control and Optimization,Aerospace Engineering,Automotive Engineering,Control and Systems Engineering

Link

https://www.worldscientific.com/doi/pdf/10.1142/S2301385015500041

Reference10 articles.

1. A Formal Analysis and Taxonomy of Task Allocation in Multi-Robot Systems

2. Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms

3. Cooperative Task Assignment/Path Planning of Multiple Unmanned Aerial Vehicles Using Genetic Algorithm

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