Target Enclosing Control of Unmanned Aerial Vehicle Swarm Based on Socialized Collaboration
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摘要: 面向感知、通信受限且存在环境障碍的移动目标合围控制, 提出一种基于社会化协同的控制策略. 首先, 借鉴生物集群社会化行为, 构建协同响应模型与层级交互机制; 在拓扑切换与丢包条件下, 显式建模受限信息流, 以驱动集群实现目标合围. 其次, 提出强引导式任务——避碰并行协同控制, 在优先保障飞行安全的前提下实现鲁棒合围控制. 再次, 设计一致性目标状态观测器, 对目标位置与速度进行稳健估计. 最后, 仿真结果表明, 所提方法在障碍环境以及感知、通信受限条件下能够实现稳定合围, 并表现出较好的鲁棒性.Abstract: A socialized collaboration-based control strategy is proposed for mobile-target enclosing under perception and communication constraints in the presence of environmental obstacles. First, inspired by socialized behaviors in biological swarms, a cooperative response model and a hierarchical interaction mechanism are established; under topology switching and packet-loss conditions, the constrained information flow is explicitly modeled to drive the swarm to achieve target enclosing. Second, a strongly guided task——Collision-avoidance parallel cooperative control scheme is proposed to realize robust enclosing control while prioritizing flight safety. Third, a consensus-based target-state observer is designed to robustly estimate the target position and velocity. Finally, simulation results demonstrate that the proposed method can achieve stable encirclement in obstacle-laden environments under perception and communication constraints, exhibiting favorable robustness.
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图 8 不同失效比例下的避碰成功率
Fig. 8 Collision-avoidance success rate under different failure ratios
表 1 仿真参数设置
Table 1 Settings of simulation parameters
类别 符号 数值 无人机集群规模 $ N $ $ 7 $ 速度上下界 $ ({V}_{\min },\;{V}_{\max }) $ $ (10,\;80)\;\mathrm{m}/\mathrm{s} $ 最大过载 $ {n}_{\max } $ $ 6 $ 最大航迹角 $ {\gamma }_{\max } $ $ \pi /4 $ 自动驾驶仪时间常数 $ {\tau }_{v},\;{\tau }_{\chi },\;{\tau }_{\gamma } $ $ 2.5\;\mathrm{s} $, $ 2.5\;\mathrm{s},\;2.5\;\mathrm{s} $ 高度控制增益常数 $ {k}_{1},\;{k}_{2} $ $ 2,\;2 $ 感知半径 $ {r}_{d} $ $ 200\;\mathrm{m} $ 通信距离 $ {r}_{c} $ $ 200\;\mathrm{m} $ 期望合围半径 $ {r}^{\mathrm{*}} $ $ 100\;\mathrm{m} $ 期望角间距 $ \varphi _{iT}^{\mathrm{*}} $ $ -\pi $ 观测器收敛系数 $ \alpha $ $ 2 $ 排斥力系数 $ {\rho }_{a} $ $ 2 $ 安全距离 $ {d}_{s} $ $ 150\;\mathrm{m} $ 控制增益 $ {\gamma }_{1},\;{\gamma }_{2},\;{\gamma }_{3},\;{\gamma }_{4} $ $ 5,\;1,\;5,\;8 $ 社会力权重系数 $ {w}_{co},\;{w}_{T},\; $$ {w}_{d},\;{w}_{w} $ $ 0.40,\;0.40,\; $$ 0.15,\;0.05 $ 
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表 2 障碍物参数设置
Table 2 Settings of obstacle parameters
障碍物标号 中心点坐标 范围半径 1 (400, 280) 150 m 2 (800, −280) 50 m 3 ( 1200 , 150)150 m 4 ( 1600 , −150)50 m 5 ( 2300 , 150)150 m
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表 3 三种算法对比仿真统计结果
Table 3 Simulation statistics results for three algorithms comparison
指标 VFM RFM 本文 合围时间$ {T}_{c}\;(\mathrm{s}) $ 82.4 ± 6.8 95.7 ± 9.3 59.3 ± 5.4 稳态半径均方误差$ {E}_{r}\left(\mathrm{m}\right) $ 5.1 ± 1.7 6.8 ± 2.1 3.2 ± 1.0 避碰成功率$ {P}_{c}\;(\% ) $ 71.2 ± 4.9 65.5 ± 6.3 93.8 ± 3.1 RMSE 3.5 ± 0.8 4.1 ± 1.0 2.4 ± 0.7
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