GNSS/INS/VNS组合定位信息融合的多无人机协同导航方法

doi:10.12382/bgxb.2023.0860

摘要/Abstract

摘要：

在信息化战场上,无人机面临多种潜在威胁,不时出现的非意图干扰对无人机系统的卫星信号和通信链路造成干扰,对飞行产生不良影响。为了解决这一挑战,采用多传感器信息融合,以全球导航卫星系统(Global Navigation Satellite System,GNSS)和惯性导航系统(Inertial Navigation System, INS)组合导航系统为主滤波器,并将全球定位系统导航系统和视觉导航系统作为子滤波器,建立了联合滤波器。将多架无人机数字影像的相对导航信息与各无人机平台获取的绝对导航信息融合,实现了一种基于卡尔曼滤波的多地标接力辅助导航算法,有效提高了GNSS/INS相对导航系统的解算精度,降低了多无人机群体的计算负担,扩大了无人机的巡航范围。采用并行分布式的系统框架,将算法部署在多个无人机平台上,通过无人机之间的信息传递和互动,实现多无人机的协同感知与自主定位。在仿真任务场景中进行相关实验,实验结果显示该方法在3架无人机协同导航中位置估计平均误差达到0.66m,速度估计精度保持在±0.4m/s,满足设计要求。

关键词: 无人机, 协同导航, 卡尔曼滤波算法, GNSS/INS组合, 视觉导航

Abstract:

In the informationized battlefield, the unmanned aerial vehicles (UAVs) face various potential threats, including intermittent unintentional interference that disrupts the satellite signals and communication links of unmanned aircraft systems (UAS), leading to adverse effects on flight. To address this challenge, the multi-sensor data is utilized, and a joint filter is established usingthe global navigation satellite system (GNSS) and inertial navigation system (INS) combination navigation system as the main filter, and the global positioning system (GPS) and visual navigation system (VNS) as sub-filters. This approach fuses the relative navigation information from multiple UAVs’ digital image maps with the absolute navigation information acquired by each UAV platform, creating a Kalman filter-based multi-landmark relay-assisted navigation algorithm. This effectively enhances the solution accuracy of GNSS/INS relative navigation systems, reduces the computational burden on multi-UAVs, and expands the cruising range of UAVs. Additionally, a parallel distributed system framework is used to deploy the algorithm on multiple UAV platforms and facilitatethe information sharing and interaction among UAVs, thereby achieving the collaborative perception and autonomous positioning of multi-UAVs. The experiments conducted in simulated mission scenarios demonstrate that this approach meets the precision requirements, achieving an average positional estimation error of 0.66 mand maintaining the velocity estimation accuracy within ±0.4m/s in the collaborative navigation of three UAVs.

Key words: unmanned aerial vehicles, cooperative navigation, Kalman filter algorithm, GNSS/INS integration, visual navigation

中图分类号:

曹正阳, 张冰, 白屹轩, 勾柯楠. GNSS/INS/VNS组合定位信息融合的多无人机协同导航方法[J]. 兵工学报, 2023, 44(S2): 157-166.

CAO Zhengyang, ZHANG Bing, BAI Yixuan, GOU Kenan. Multi-UAV Cooperative Navigation Method Based on Fusion of GNSS/INS/VNS Positioning Information[J]. Acta Armamentarii, 2023, 44(S2): 157-166.

图/表 16

图1 无人机协同导航系统结构示意图

Fig.1 Schematic diagram of multi-UAV cooperative navigation system

图2 融合导航框架

Fig.2 Schematic diagram of collaborative navigation framework

图3 伪距差分定位示意图

Fig.3 Pseudorange differential positioning diagram

图4 节点间信息传递方式示意图

Fig.4 Schematic diagram of information exchange between nodes

表1 导航设备参数

Table 1 Navigation devices parameters

陀螺仪	零漂:0.1(°)/h	随机游走:0.01(°)/√h
加速度计	零漂:100μg	随机游走:5μg/√Hz
GPS	伪距误差:3m	伪距率误差:0.03m/s
光电传感器	稳定精度:5μrad	精度误差:0.35mm/pixel

图5 卡尔曼滤波器速度估计误差对比图

Fig.5 Comparison of speed estimation errors of Kalman filtering

图6 GNSS/INS/VNS组合位置估计距离误差测试图

Fig.6 GNSS/INS/VNS position estimation error Testing

图7 GPS位置估计距离误差测试图

Fig.7 GPS position estimation error testing

表2 定位误差对比

Table 2 Positioning error comparison

定位方式	平均误差/m
GPS	0.66
GNSS/INS/VNS组合	0.25

图8 速度估计误差测试图

Fig.8 Velocity estimation error Testing

图9 多无人机协同导航飞行测试示意图

Fig.9 Schematic diagram of multi-UAVcollaborative navigation flight testing

图10 协同导航算法的鲁棒性对比测试图

Fig.10 Robustness comparison test chart of collaborative navigation algorithms

图11 协同导航位姿漂移特性测试图

Fig.11 Test charts of collaborative navigation pose drift characteristics

表3 无人机1绝对位置误差

Table 3 UAV 1absolute position error

方向	最大偏差/m	平均偏差/m	中间误差/m
x	1.324	0.483	0.467
y	6.057	1.948	1.985
z	1.380	0.594	0.617

表4 无人机2绝对位置误差

Table 4 UAV 2 absolute position error

方向	最大偏差/m	平均偏差/m	中间误差/m
x	1.544	0.563	0.559
y	3.249	1.873	1.867
z	1.651	0.682	0.674

表5 无人机3绝对位置误差

Table 5 UAV 3 absolute position error

方向	最大偏差/m	平均偏差/m	中间误差/m
x	1.475	0.522	0.507
y	2.931	1.827	1.796
z	1.275	0.537	0.512

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