摘要
扫描覆盖问题一直是无线传感网络的热点问题,目前大多数研究主要集中于同构无人机扫描覆盖问题,目标为无人机数量最小的情况下对区域节点达到全覆盖。近年来,扫描覆盖问题衍生出新的方向,即MTMC(min-time max-coverage)问题,即使用有限无人机对区域节点进行扫描覆盖,使得覆盖率尽可能大的同时任务时间尽可能小。在考虑了无人机异构性的基础上,分析了MTMC问题的数学模型,提出了CWBGAA(CW Based on Genetic Annealing Algorithm optimization)算法解决MTMC问题。上述算法分为两阶段解决问题,第一阶段基于启发式插入算法生成每架无人机对应的飞行路径,第二阶段基于遗传退火算法对生成后路径进行路径优化,使得无人机的飞行时间降低。仿真结果表明,CWBGAA算法相较于其它算法拥有更好的性能,提升覆盖率的同时降低了任务执行时间。
The scanning coverage problem has always been a hot issue in wireless sensor networks.At present,most researches focus on the scanning coverage problem of isomorphic UAVs.The goal is to achieve full coverage of regional nodes when the number of UAVs is the smallest.In recent years,the scan coverage problem has derived a new direction,that is,the min-time max-coverage(MTMC)problem,that is,to use limited UAVs to scan and cover regional nodes,so that the coverage rate is as large as possible and the task time is as small as possible.On the basis of considering the heterogeneity of the UAV,the mathematical model of the MTMC problem is analyzed,and the CW Based on Genetic Annealing Algorithm optimization(CWBGAA)algorithm is proposed to solve the MTMC problem.The algorithm is divided into two stages to solve the problem.The first stage is based on the heuristic insertion algorithm to generate the flight path corresponding to each drone.The second stage is based on the genetic annealing algorithm to optimize the path after generation,so that the flight time of the drone reduce.The simulation results show that the CWBGAA algorithm has better performance than other algorithms,and it improves the coverage while reducing the task execution time.
作者
蓝浩
陶伟
李辉
LAN Hao;TAO Wei;LI Hui(College of Computer Science(Software Engineering College),Sichuan University,ChengduSichuan 610065,China;Sichuan University National Key Laboratory of Fundamental Science on Synthetic Vision,ChengduSichuan 610065,China;China Ship Research and Design Center,Wuhan Hubei 430064,China)
出处
《计算机仿真》
2025年第2期405-411,共7页
Computer Simulation
基金
国家自然科学基金重点项目(U20A20161)。
