排放控制区巡检作业中异质无人机与无人艇协同调度的双层规划  

Two-Layer Planning Approach for Coordinated Scheduling of Heterogeneous UAVs and USVs in Emission Control Area Inspection Operations

作　　者：胡志华[1] 牛雅凡 HU Zhihua;NIU Yafan(Logistics Research Center,Shanghai Maritime University,Shanghai 201306,China)

机构地区：[1]上海海事大学物流研究中心,上海201306

出　　处：《华南师范大学学报(自然科学版)》2024年第6期65-75,共11页Journal of South China Normal University(Natural Science Edition)

基　　金：国家自然科学基金项目(71871136);上海市自然科学基金面上项目(23ZR1426500)。

摘　　要：为提高无人机巡检船舶尾气排放效率,考虑了异质无人机与无人艇协同调度路径规划问题,采用双层规划模型解决无人机与无人艇配置和路径优化问题。上层模型利用改进的自适应模拟退火算法求出无人机与无人艇配置方案;下层模型以最短无人机飞行总时间为目标,结合无人机、无人艇和船舶实时动态性特征,利用改进的遗传算法求出无人机检测序列与飞行路径和无人艇航行路径。算例实验结果表明:异质无人机与无人艇协同调度相较于固定配置下同质无人机与无人艇在飞行总时间上平均减少24.98%,无人机成本平均减少23.57%,无人艇成本平均减少42.20%;无人机速度每提升15 km/h,飞行总时间平均减少23.02%,无人机成本平均增加26.45%;无人艇速度每提升5 km/h,无人艇成本平均增加37%。To enhance the efficiency of Unmanned Aerial Vehicles(UAVs)in inspecting ship emissions,this study addresses the coordinated scheduling and routing planning problem for heterogeneous UAVs and Unmanned Surface vessels(USVs),employing a two-layer planning model to tackle the configuration and path optimization issues.The upper-layer model implements an improved adaptive simulated annealing algorithm to determine the allocation of UAVs and USVs.Meanwhile,the lower-layer model seeks to minimize the total flight time of UAVs by integrating the real-time dynamic characteristics of UAVs,USVs,and ships,utilizing an improved genetic algorithm to formulate the UAV detection sequence,flight path,and USV navigation path.Experimental results indicate that the coordinated scheduling of heterogeneous UAVs and USVs significantly reduces average flight time by 24.98%,UAV costs by 23.57%,and USV costs by 42.20%when compared to fixed configurations with homogeneous UAVs and USVs.Additionally,increasing UAV speed by 15 km/h results in an average reduction of 23.02%in flight time,while the UAV cost rises by 26.45%.Furthermore,for every 5 km/h increase in USV speed,the average USV cost escalates by 37%.

关 键 词：无人机巡检 选址与路径优化 双层规划模型 改进模拟退火算法 改进遗传算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]