Multi-UAV Regional Patrol Mission Planning Strategy
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摘要: 目前应急搜索无人机(UAV)集群存在搜索效率低、覆盖完整性低、多机组网稳定性差等问题。对此，该文提出一种基于优化模糊C聚类算法(O-FCMA)结合优化混合粒子群算法(O-HPSO)的终端-路由UAV区域搜索任务规划策略。以UAV监测区域范围为基础，通过建立搜索区域的空间模型，进一步运用O-FCMA进行区域几何划分，并采用O-HPSO实现划分区域内的路径规划，以实现多UAV集群搜索总体任务的规划。仿真实验结果表明，采用O-HPSO结合O-FCMA进行无源UAV区域搜索任务较ACO或模拟退火算法结合K聚类算法或FCMA相比，在保证搜索区域全覆盖条件下，有源搜索与无源搜索过程中UAV决策时间分别降低了7%～21%和16%～31%，搜索效率分别提升了7%～13%和3%～7%。结果表明所提方法有效降低了UAV集群的决策时间，提升了搜索效率。Abstract: At present, the emergency search Unmanned Aerial Vehicle (UAV) cluster has problems such as low search efficiency, low coverage integrity, and poor stability of multi-unit network. In this regard, a terminal-routing UAV area search task planning strategy based on Optimized Fuzzy C-Means Algorithm (O-FCMA) combined with Optimize-Hybrid Particle Swarm Optimization (O-HPSO) algorithm is proposed. Based on the scope of UAV monitoring area, by establishing the spatial model of the search area, this paper further uses O-FCMA to divide the area geometrically, and uses O-HPSO to realize the path planning in the divided area, so as to realize the planning of the overall task of multi-UAV cluster search. The simulation experiment is performed. The use of O-HPSO combined with O-FCMA for passive UAV area search task is compared with ACO or simulated annealing algorithm combined with K clustering algorithm or FCMA, under the condition of ensuring the full coverage of the search area, active search compared with non-active UAV, the UAV decision time is reduced by 7%～21%, 16%～31%, and the search efficiency is increased by 7%～13%, 3%～7%. The method reduces effectively the UAV cluster decision time and improves the search efficiency.
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表 1 总体方法中常数参数数值及说明
参数 说明 取值 $ v $ UAV行驶恒定速度 2.1 $ {v_r} $ 经过重复区域飞行速度 5.3 $ a $ 总体平均搜索效率权重 0.7 $ b $ 成功搜索UAV搜索效率权重 0.3 $ {\mu _1} $ 总计时间行驶距离权重 3.5 $ {\mu _2} $ 总计时间经过重复飞行区域权重 0.5 $ \alpha $ 适应度函数时间变量权重 0.1 $ \beta $ 适应度函数空间变量权重 0.9 $ \sigma $ 加入当前节点距离最近节点欧氏距离后的影响权重 0.06 $ \xi $ 搜索区域划分标准 0.1 
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表 2 有源搜索对比试验结果
算法名称 成功搜索UAV
距离(m)总搜索
效率算法寻优
时间(s)本文算法 106.1 0.93 38 ACO-K聚类算法 104.8 0.82 46 模拟退火算法 -K聚类算法 117.2 0.87 42 ACO-FCMA 104.4 0.85 41 模拟退火算法-FCMA 107.6 0.82 46
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表 3 无源搜索对比试验结果
算法名称 UAV行驶距离 总搜索效率 算法寻优
时间(s)最大 最小 本文算法 156 70 0.95 42 ACO-K聚类算法 175 71 0.89 50 模拟退火算法 -K聚类算法 165 82 0.92 49 ACO-FCMA 170 75 0.91 55 模拟退火算法-FCMA 175 74 0.88 48
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