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

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 4369201
• Main Authors: Tan, Wei, Hu, Yongjiang, Zhao, Yuefei, Li, Wenguang, Li, Yongke, Zhang, Xiaomeng
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; John Wiley & Sons, Inc
• 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
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2021/4369201

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.