Heterogeneous Multi UAV Mission Planning Based on Ant Colony Algorithm Powered BP Neural Network

With the development of modern science and technology, the field of UAV has also entered the era of high-tech exploration. Among them, the task planning, allocation, path exploration, and algorithm optimization of heterogeneous multi UAV technology are our main concerns. Based on the above situation...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2021, Vol.2021 (1), p.4369201-4369201
Main Authors: Tan, Wei, Hu, Yongjiang, Zhao, Yuefei, Li, Wenguang, Li, Yongke, Zhang, Xiaomeng
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Ant colony optimization
Back propagation networks
Cooperation
Decision making
Flight
Flight tests
Genetic algorithms
Mathematical optimization
Mathematical programming
Mission planning
Models, Theoretical
Neural networks
Neural Networks, Computer
Optimization algorithms
Planning
Sensors
Unmanned aerial vehicles
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Summary:With the development of modern science and technology, the field of UAV has also entered the era of high-tech exploration. Among them, the task planning, allocation, path exploration, and algorithm optimization of heterogeneous multi UAV technology are our main concerns. Based on the above situation, this paper proposes a heterogeneous multi UAV task planning technology based on ant colony algorithm powered BP neural network. The planning, research, and design are mainly carried out according to the actual situation of the UAV flight test, and the mathematical programming model is established according to the UAV load degree and maximum flight distance as constraints. This paper focuses on the contribution of the ant colony optimization algorithm to benefit maximization and task minimization. The experimental results show that the BP neural network optimized by the ant colony algorithm can improve the number of iterations and training time. Compared with some comparative algorithms, its performance is better.
ISSN:1687-5265
1687-5273
DOI:10.1155/2021/4369201