面向集群协同的两点相对定位技术

doi:10.12382/bgxb.2023.0829

摘要/Abstract

摘要：

无人集群节点间精确位置获取是集群协同的基础,但在复杂多变的应用环境中,全球导航卫星系统(GNSS)难以提供稳定准确的位置信息;难以部署辅助锚点;传统相对定位方法大多存在节点数量限制。针对上述3个问题,提出了一种GNSS拒止条件下的集群节点相对定位的新方法。以搭载惯性测量单元(IMU)和超宽带传感器的两个节点为例建立了相对定位模型,采用扩展卡尔曼滤波算法,融合机载IMU和机间距离信息,实现了相对位置的最优估计。仿真实验结果表明:在无人机相距200m的范围内,所提方法可达到约1.3m的相对定位精度,与现有多节点相对定位算法相比,提高了约4倍;在无需GNSS和辅助锚点的支持下,即可实现两个节点之间的高精度相对定位,能够为无人集群在复杂应用环境下的协同定位提供有效可行的解决方案。

关键词: 相对定位, 测距, 惯性测量单元, 扩展卡尔曼滤波, 卫星拒止

Abstract:

Precise inter-node position acquisition is the basis for coordination in unmanned vehicle clusters, but the global navigation satellite system (GNSS) struggles to provide the stable and accurate position information in complex and dynamic application environments. It is difficult to deploy the auxiliary anchor points, and most traditional relative positioning methods have limitations on the number of nodes. A new relative localization method for cluster nodes under GNSS-denied conditions is proposed to address the above three problems. Taking two nodes equipped with inertial measurement units (IMUs) and ultra-wideband sensors as an example, a relative positioning model is established, and an extended Kalman filter algorithm is used to fuse IMU and inter-node distance information for the optimal estimation of relative position. Simulation experiments show that the proposed method is used to achieve a relative positioning accuracy of about 1.3m within 200m range between unmanned aerial vehicles, improving nearly 4 times over existing multi-node relative positioning algorithms. The high-precision relative positioning between two nodes can be achieved without relying on GNSS or auxiliary anchor points, providing an effective and feasible solution for collaborative positioning of unmanned vehicle clusters in complex application environment.

Key words: relative positioning, ranging, inertial measurement unit, extended Kalman filter, GNSS-denied

中图分类号:

V249.32⁺8

邓廷祥, 任鹏, 程甲, 汪建兵, 梁振杰, 相征. 面向集群协同的两点相对定位技术[J]. 兵工学报, 2023, 44(S2): 22-34.

DENG Tingxiang, REN Peng, CHENG Jia, WANG Jianbing, LIANG Zhenjie, XIANG Zheng. Relative Positioning Technology for Two Points Based on Cluster Cooperative Orientation[J]. Acta Armamentarii, 2023, 44(S2): 22-34.

图/表 17

图1 无人机相对定位系统

Fig.1 UAV relative positioning system

图2 整体系统的流程

Fig.2 Working process of the whole system

表1 仿真实验参数

Table 1 Simulation experiment parameters

参数	数值
IMU采样率/s	0.005
陀螺仪常值漂/(deg·h^-1)	0.1
加速度计常值漂移/ug	100
测距误差/m	0.5
初始位置误差/m	(2,2,2)
初始速度误差/(m·s^-1)	(0.1,0.1,0.1)
UWB位置(x,y,z)/m	UWB₁(0.4,0,0) UWB₂(-0.3,0,0) UWB₃(0,0.3,0.4)
状态噪声矩阵	diag(10^-5×I_3×1;10^-2×I_3×1; 10^-8×I_6×1)

表2 运动参数设置

Table 2 Motion parameter setting

运动参数	数值
飞行时长/s	600
初始相对位置/m	18
初始速度/(m·s^-1)	0

图3 无人机相对运动轨迹

Fig.3 Relative motion trajectory of UAV

图4 无人机间距离值

Fig.4 Distance between UAVs

图5 惯性导航的相对位置误差

Fig.5 Relative position error of inertial navigation

图6 相对定位算法仿真

Fig.6 Simulation of relative positioning algorithm

图7 相对定位算法与惯性导航的对比

Fig.7 Comparison of relative positioning algorithm and inertial navigation

图8 距离对相对定位性能的影响

Fig.8 The effect of distance on relative positioning performance

表3 UWB传感器设置

Table 3 UWB sensor settings

UWB传感器编号	位置(x,y,z)/m
UWB1	(0.4,0,0)
UWB2	(-0.3,0,0)
UWB3	(0,0.3,0.4)
UWB4	(0,-0.5,0)
UWB5	(0,0,-0.5)

图9 不同数量UWB的相对定位结果

Fig.9 Relative positioning results of different number of UWB sensors

图10 两算法定位误差对比

Fig.10 Comparison of positioning errors of two algorithms

图11 示例样机

Fig.11 Sample prototype

图12 系统样机部署

Fig.12 Deployment of system prototypes

图13 试验得到的无人机位置值

Fig.13 UAV position value obtained from the test

图14 无人机的位置误差

Fig.14 Position error of UAV

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