蚁群算法优化的无人机路径规划  

Path Planning for UAV Based on Ant Colony Algorithm

作　　者：闫静 叶婷[1] YAN Jing;YE Ting(Xi’an Aeronautical Polytechnic Institute,Xi’an Shaanxi 710000,China)

机构地区：[1]西安航空职业技术学院通用航空学院,陕西西安710000

出　　处：《信息与电脑》2024年第10期172-174,共3页Information & Computer

基　　金：中国高校产学研创新基金:UAV-WSN中无人机路径规划方法研究(项目编号:2022IT175);西安航空职业技术学院引进高层次人才科研启动基金:无人机辅助WSN的路径规划算法研究(项目编号:2023XHQDJ03)。

摘　　要：无人机(Unmanned Aerial Vehicle,UAV)由于其移动性和灵活性,被广泛应用于农业、自然环境考察、公共安全、科学研究等诸多领域的生产和研究。无线射频传输能为广大物联网设备提供可靠、具有经济效益的能源供应,而能利用无人机同时为多个无线设备充电的信能同传系统,其需求正呈现爆发式增长趋势。本文研究了一种无人机辅助无线信能同传通信网络,其中无人机作为一个移动基站,定期为一组地面传感器提供服务。无人机利用射频无线能量传输,为下行链路的地面传感器充电,地面传感器利用收集到的射频能量向上行链路的无人机发送先前收集到的独立数据信息。无人机辅助的无线射频信能同传系统可以通过设计无人机轨迹,充分利用无人机的机动性,结合无线资源分配优化,达到最大化系统吞吐量。仿真结果表明,利用蚁群算法进行路径规划能有效地降低无人机的总能耗。Due to mobility and flexibility,UAVs are widely used in production and research in many fields such as agriculture,environment,public safety,and scientific research.Wireless radio-frequency transfer can provide a reliable and cost-effective energy for IoT devices.Demands for simultaneous information and energy transmission that use UAVs to charge for devices is showing an explosive growth trend.This paper studies a UAV-assisted wireless network,in which the UAV acts as a mobile base station and regularly provides energy to sensors.The UAV uses RF wireless energy transmission to charge the sensors on the downlink,and then sensors use the collected energy to send information to the UAVs on the uplink.This system can maximize system throughput by designing the UAV trajectory,making full use of the UAV’s mobility,and optimizing wireless resource allocation.The simulation results show that using the ant colony algorithm for path planning can effectively reduce the total energy consumption of UAVs.

关 键 词：无人机 无线能量 蚁群算法 路径规划 

分 类 号：R279[医药卫生—中医学]