Particle Swarm Optimization Bat Algorithm Path Automatically Planning Research for Police Drones in Hilly Cities  

作　　者：Jing XUE Zefu TAN Nina DAI Guoping LEI Chao HE 

机构地区：[1]Chongqing Three Gorges University,Chongqing 404020,China

出　　处：《Journal of Systems Science and Information》2024年第1期125-144,共20页系统科学与信息学报（英文）

基　　金：Supported by the Science and Technology Research Program of Chongqing Municipal Education Commission(KJZD-K202201203);Chongqing Three Gorges University and the Public Security Bureau of Wanzhou District,Chongqing(wzstc20210302,wzstc20220310)。

摘　　要：Mountain cities are complex asymmetric dynamic network architectures,and the flight of UAVs in this environment is subject to various constraints,while efficiency is a crucial factor in the trajectory planning of police UAVs,which need to maintain high efficiency and safe flight paths between their starting and ending points,but the traditional trajectory planning method cannot meet the requirements of rapid maneuvering of police UAVs.To achieve this,a 3D terrain map is built,an objective function is established for the flight cost in the UAV trajectory planning process,and a planning algorithm called particle swarm optimization bat algorithm(PSOBA)is proposed.PSOBA combines the characteristics of the bat algorithm(BA)and the particle swarm optimization algorithm(PSO)to improve population diversity and resolve the delayed convergence issue in the last phases of BA.Simulation results show that PSOBA is more effective than BA,with a search time for the best solution that is approximately 20.43%shorter and a convergence value of the objective function that is approximately 38%smaller.PSOBA is also able to plan a quicker,shorter,and safer flight path compared to other trail planning algorithms that enhance the bat algorithm.These findings suggest that PSOBA is a powerful algorithm with potential application value in UAV trajectory planning control in the mobile intelligence era.Contribute to the service of public social security.

关 键 词：hilly cities police drones asymmetrical trajectory planning particle swarm bat algorithm 

分 类 号：V279[航空宇航科学与技术—飞行器设计] V249[自动化与计算机技术—控制理论与控制工程] TP18[自动化与计算机技术—控制科学与工程]