Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method

An optimal mission assignment and path planning method of multiple unmanned aerial vehicles (UAVs) for disaster rescue is proposed. In this application, the UAVs include the drug delivery UAV, image collection UAV, and communication relay UAV. When implementing the modeling and simulation, first, th...

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Bibliographic Details
Published inActuators Vol. 11; no. 1; p. 4
Main Authors Liu, Haoting, Ge, Jianyue, Wang, Yuan, Li, Jiacheng, Ding, Kai, Zhang, Zhiqiang, Guo, Zhenhui, Li, Wei, Lan, Jinhui
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2022
Subjects
Adaptive algorithms
Analytic hierarchy process
artificial bee colony
Communication
disaster rescue
Disasters
Evacuations & rescues
genetic algorithm
Genetic algorithms
Iterative methods
Mathematical programming
mission assignment
multiple UAVs
Oil consumption
Optimization
Path planning
Planning
Rain
Remote sensing
Revenue
Swarm intelligence
Transmission towers
Unmanned aerial vehicles
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Summary:An optimal mission assignment and path planning method of multiple unmanned aerial vehicles (UAVs) for disaster rescue is proposed. In this application, the UAVs include the drug delivery UAV, image collection UAV, and communication relay UAV. When implementing the modeling and simulation, first, three threat sources are built: the weather threat source, transmission tower threat source, and upland threat source. Second, a cost-revenue function is constructed. The flight distance, oil consumption, function descriptions of UAV, and threat source factors above are considered. The analytic hierarchy process (AHP) method is utilized to estimate the weights of cost-revenue function. Third, an adaptive genetic algorithm (AGA) is designed to solve the mission allocation task. A fitness function which considers the current and maximum iteration numbers is proposed to improve the AGA convergence performance. Finally, an optimal path plan between the neighboring mission points is computed by an improved artificial bee colony (IABC) method. A balanced searching strategy is developed to modify the IABC computational effect. Extensive simulation experiments have shown the effectiveness of our method.
ISSN:2076-0825
2076-0825
DOI:10.3390/act11010004