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This paper conducts an in-depth exploration of UAV communication networking and path planning within geographically constrained environments. The study begins with necessary data preprocessing, which includes the conversion of latitude and longitude coordinates and the organization of antenna parameters. Through the development of a model based on coordinate geometry, this research effectively identifies communication blind spots and establishes a comprehensive geometric envelope for signal coverage. To enhance network efficiency and path planning, the paper introduces a hybrid optimization algorithm that combines simulated annealing and genetic algorithms. Simulated annealing is used to optimize the selection of communication types between network nodes, while genetic algorithms refine the UAV fleet's flight routes based on these communication strategies. This dual-layered approach allows for fine-tuned adjustments in response to dynamic environmental constraints. The effectiveness of this method is demonstrated through simulations, which reveal a communication coverage rate of 73.7272%. These results confirm that the proposed hybrid optimization technique significantly improves both communication coverage and operational efficiency of UAV networks in complex environments. This provides substantial theoretical insights and practical contributions to the field of UAV network optimization, which is particularly valuable for applications in remote or urban areas where geographical constraints pose significant challenges.
Zhennan Ma (Mon,) studied this question.