High-reliability Automatic Collision Avoidance Strategy for Unmanned Aerial Vehicles Based on Sensing Reconstruction
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摘要: 面向低空经济发展中无人飞行器对复杂空域安全飞行的需求, 系统考虑大气传感器在强风干扰下的失效问题, 并提出一种基于传感重构的高可靠自动防撞策略. 首先建立含湍流扰动的飞行器动力学模型, 采用自适应容积卡尔曼滤波融合导航量测与控制信号, 实现真空速与气流角等状态的鲁棒在线重构; 其次针对逃逸阶段的模型失配与噪声扰动, 设计智能学习自适应控制律补偿状态估计误差, 实现逃逸姿态指令稳定跟踪; 最后构建滤波协方差驱动的动态碰撞包络, 结合控制系统模型量化轨迹预测不确定度, 完成地形碰撞检测, 并生成多逃逸轨迹择优避障指令. 仿真结果表明, 在突风与强湍流条件下, 可实现气流角精确重构及鲁棒防撞告警与改出控制, 相关技术可为低空无人飞行器防撞系统设计提供可靠方案.Abstract: Addressing the safety flight requirements of unmanned aerial vehicles in the context of low-altitude economy development, this paper systematically considers the failure issue of atmospheric sensors under strong wind interference and proposes a high-reliability automatic collision avoidance strategy based on sensing reconstruction. Firstly, an aircraft dynamics model incorporating turbulence disturbances is established, and an adaptive cubature Kalman filter is employed to fuse navigation measurements and control signals, achieving robust online reconstruction of states such as true airspeed and airflow angles. Secondly, to address model mismatch and noise disturbances during the escape phase, an intelligent learning-based adaptive control law is designed to compensate for state estimation errors, enabling stable tracking of escape maneuver commands. Finally, a dynamic collision envelope driven by the filter covariance is constructed, and trajectory prediction uncertainty is quantified by integrating the control system model to complete terrain collision detection. This facilitates the generation of optimal obstacle avoidance commands by evaluating multiple escape trajectories. Simulation results show that accurate airflow angle reconstruction and robust collision warning and recovery control are achieved under gust and severe turbulence conditions. The related techniques can provide a reliable solution for the design of collision avoidance systems in low-altitude unmanned aerial vehicles.
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图 1 基于传感重构的高可靠无人飞行器自动防撞策略的整体技术框图
Fig. 1 System block diagram of the high-reliability automatic collision avoidance strategy for unmanned aerial vehicles based on sensing reconstruction
图 3 真空速重构结果((a)估计结果; (b)估计误差)
Fig. 3 Reconstruction result of $V$((a)Estimation result; (b)Estimation error)
图 5 侧滑角重构结果((a)估计结果; (b)估计误差)
Fig. 5 Reconstruction result of $\beta$((a)Estimation result; (b)Estimation error)
图 4 迎角重构结果((a)估计结果; (b)估计误差)
Fig. 4 Reconstruction result of $\alpha$((a)Estimation result; (b)Estimation error)
图 6 风作用估计结果((a)${V}_{wx}^{g}$; (b)${V}_{wy}^{g}$; (c)${V}_{wz}^{g}$)
Fig. 6 Estimation result of ${\boldsymbol{V}}_{w}^{g}$((a)${V}_{wx}^{g}$; (b)${V}_{wy}^{g}$; (c)${V}_{wz}^{g}$)
图 7 逃逸姿态指令跟踪结果((a)$\phi $; (b)$\theta $)
Fig. 7 Result of escape attitude command tracking((a)$\phi $; (b)$\theta $)
图 9 预测轨迹碰撞检测结果三维展示
Fig. 9 Results of 3 D visualization of predicted trajectory collision detection
图 10 预测轨迹碰撞检测结果二维展示((a)左转爬升轨迹; (b)垂直爬升轨迹; (c)右转爬升轨迹; (d)原任务轨迹)
Fig. 10 Results of 2 D visualization of predicted trajectory collision detection((a)Left-turn climb trajectory; (b)Vertical climb trajectory; (c)Right-turn climb trajectory; (d)Original mission trajectory)
表 1 轨迹预测不确定度估计算法
Table 1 Algorithm for estimating uncertainty in trajectory prediction
算法1. 轨迹预测不确定度估计算法 输入. 当前为$k$时刻, $\hat{{\boldsymbol{X}}}_{k}$, $\hat{{\boldsymbol{P}}}_{k}$, $\hat{{\boldsymbol{Q}}}_{k}$, $\phi _{c}$, $\theta _{c}$, $\Delta t$, $T$ 初始化. ${{\boldsymbol{P}}}_{p,\; 0}=[\hat{{X}}_{k,\; 1},\; \hat{{X}}_{k,\; 2},\; \hat{{X}}_{k,\; 3}]^{{\rm{T}}}$ ${{\boldsymbol{S}}}_{p,\; 0}=2.58[\hat{{P}}_{k,\; 1},\; \hat{{P}}_{k,\; 2},\; \hat{{P}}_{k,\; 3}]^{{\rm{T}}}$ ${\rm{For}}$ $j = 1,\; 2,\; 3,\; \cdots ,\; T_{p}/\Delta t-1$ ①根据式(32)计算$\phi _{c}$, $\theta _{c}$ ②根据式(35)、(36)、(41)、(44)、(45)、(46)计算$\overline{\boldsymbol{\delta}}$ ③令${{\boldsymbol{U}}}_{k}=[T,\; \overline{\boldsymbol{\delta}}^{{\rm{T}}}]^{{\rm{T}}} $ ④根据式(17)$-$(20)计算$\hat{{\boldsymbol{X}}}_{k+1 |k}$, $\hat{{\boldsymbol{P}}}_{k+1 |k}$ ⑤令$\hat{{\boldsymbol{X}}}_{k+1}=\hat{{\boldsymbol{X}}}_{k+1 |k}$, $\hat{{\boldsymbol{P}}}_{k+1}=\hat{{{\boldsymbol{P}}}}_{k+1 |k}$, $\hat{{\boldsymbol{Q}}}_{k+1}=\hat{{\boldsymbol{Q}}}_{k}$ ⑥${{\boldsymbol{P}}}_{p,\; j}=[\hat{{X}}_{k+1,\; 1},\; \hat{{X}}_{k+1,\; 2},\; \hat{{X}}_{k+1,\; 3}]^{{\rm{T}}}$ ${{\boldsymbol{S}}}_{p,\; j}=2.58[\hat{{P}}_{k+1,\; 1},\; \hat{{P}}_{k+1,\; 2},\; \hat{{P}}_{k+1,\; 3}]^{{\rm{T}}}$ ⑦令$k=k+1$ End 输出. : ${{\boldsymbol{P}}}_{p}$, ${{\boldsymbol{S}}}_{p}$ 
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表 2 算法性能仿真测试结果
Table 2 Results of algorithm performance simulation test
评判指标 本文算法 对比算法 测试场景 高原 丘陵 高原 丘陵 虚警率 0% 0.8% 3.2% 6.0% 告警成功率 99.2% 98.0% 89.6% 85.2% 改出成功率 98.8% 97.6% 83.2% 80.8%
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