Task allocation and route planning of multiple UAVs in a marine environment based on an improved particle swarm optimization algorithm

Unmanned aerial vehicles (UAVs) are considered a promising example of an automatic emergency task in a dynamic marine environment. However, the maritime communication performance between UAVs and offshore platforms has become a severe challenge. Due to the complex marine environment, the task alloca...

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Veröffentlicht in:EURASIP journal on advances in signal processing Jg. 2021; H. 1; S. 1 - 23
Hauptverfasser: Yan, Ming, Yuan, Huimin, Xu, Jie, Yu, Ying, Jin, Libiao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 09.10.2021
Springer
Springer Nature B.V
SpringerOpen
Schlagworte:
Algorithms
Artificial Intelligence and Cognitive Computing for Smart Internet of Vehicle
Autonomous underwater vehicles
Control systems
Drone aircraft
Engineering
Genetic algorithms
Marine environment
Mathematical optimization
Offshore platforms
Optimization
Particle swarm optimization
PSO
Quantum Information Technology
Route planning
Signal,Image and Speech Processing
Spintronics
Task allocation
UAV
Unmanned aerial vehicles
China
UAV
Route planning
PSO
Task allocation
ISSN:1687-6180, 1687-6172, 1687-6180
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Zusammenfassung:Unmanned aerial vehicles (UAVs) are considered a promising example of an automatic emergency task in a dynamic marine environment. However, the maritime communication performance between UAVs and offshore platforms has become a severe challenge. Due to the complex marine environment, the task allocation and route planning efficiency of multiple UAVs in an intelligent ocean are not satisfactory. To address these challenges, this paper proposes an intelligent marine task allocation and route planning scheme for multiple UAVs based on improved particle swarm optimization combined with a genetic algorithm (GA-PSO). Based on the simulation of an intelligent marine control system, the traditional particle swarm optimization (PSO) algorithm is improved by introducing partial matching crossover and secondary transposition mutation. The improved GA-PSO is used to solve the random task allocation problem of multiple UAVs and the two-dimensional route planning of a single UAV. The simulation results show that compared with the traditional scheme, the proposed scheme can significantly improve the task allocation efficiency, and the navigation path planned by the proposed scheme is also optimal.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1186/s13634-021-00804-9