Multi-UAV Search Route Planning Based on the Improved Genetic Algorithm
Xinyu Sheng · 2024
In response to the need for target search for multiple UAVs in a specified area, this paper proposes an improved adaptive genetic optimisation algorithm, which incorporates an enhanced greedy search strategy on the basis of the original genetic algorithm in order to improve the search efficiency and target matching accuracy. Based on the pre-acquired information, this study subdivided the search area into multiple grid-like structures, and based on this, combined with the The state update cycle of UAVs and their performance limitations, a mathematical model of cooperative search is constructed. The binary coding form of 0-1 is utilised to associate the UAV's heading control sequence with the probability of the search, and for the continuous detection carried out by the airborne radar in a specific region, this study proposes an improved strategy that incorporates greedy operators and employs greedy mutation with a view to enhancing the accuracy of the search results, and in order to enhance the algorithm's performance of the local search at a later stage, a mutation strategy that can be dynamically adjusted according to the probability of the search is introduced In order to enhance the performance of the algorithm in the later local search, a mutation strategy selection threshold that can be dynamically adjusted according to the change of the search probability is introduced. The simulation experimental data illustrate that the optimised adaptive genetic algorithm has a significant improvement in the overall efficacy, the search efficiency and the robustness.