基于双目视觉的无人机自主空中对接系统设计

doi:10.7527/S1000-6893.2023.28884

Abstract

Abstract:

In this paper， a binocular vision-based Unmanned Aerial Vehicle （UAV） autonomous aerial docking system is designed for the docking segment in autonomous aerial refueling mission， which requires high accuracy， efficiency and security. Firstly， a target detection module based on binocular vision and YOLOv4-Tiny network is proposed for target detection and position estimation， and data enhancement is performed on the drogue image dataset taken under various environments. Test results of the module indicate that it can perform drogue detection and positioning in multiple intricate circumstances， maintaining the depth estimation error within 2%. After that， to satisfy the control accuracy and robustness requirements of UAV controller during the docking process in the presence of strong external environment disturbance， dynamics-modelling and disturbance-observer based active anti-disturbance control law design is carried out on a quadcopter testing platform， and the designed control law has also been verified by simulation tests. Finally， flight experiments of autonomous aerial docking are conducted on the quadcopter testing platform. Experiments results show that the autonomous aerial docking system has a properly designed structure， which can maintain attitude stability under strong external wind disturbance， perform accurate drogue detection and positioning， and complete the docking mission successfully and efficiently.

Key words: automated aerial refueling, binocular vision, autonomous aerial docking system, disturbance observer, active anti-disturbance control

CLC Number:

V249

Yifang FU, Xinyue HU, Yulu HUANG, Ban WANG, Jiangtao HUANG. A binocular vision⁃based autonomous aerial docking system design for UAVs[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(20): 628884.

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参数	数值
相机尺寸/mm	90×25×25
深度分辨率	1 280×720
深度精度	2 m处<2%
深度相机视场	87°×58°
理想检测深度/m	0.3~3
深度帧率/fps	最高90
RGB相机分辨率	1 920×1 080
RGB相机帧率/fps	30
RGB相机视场	69°×42°

参数	数值
GPU	具备48 个Tensor 核心的384 核Volta™ GPU
CPU	6核心NVIDIA Carmel ARM^® v8.2 64位CPU
内存	8 GB 128位元 LPDDR4x
存储空间	16 GB eMMC 5.1

参数名称	含义	数值
input_shape	输入图像大小	416×416
epoch	总训练世代	500
batch_size	每输入batch个样本更新一次	32
optimizer_type	优化器种类	SGD
Init_lr	起始学习率（最大）	0.01
Min_lr	最小学习率	0.01×Init_lr

序号	实际距离/mm	相机测量距离/mm	相对误差/%
1	120.0	122.0	1.64
2	164.0	164.8	0.49
3	181.0	184.2	1.78
4	201.0	204.7	1.84
5	226.0	220.0	1.77

参数	数值
总质量m/kg	1.121
电机中心距离（滚转方向）/m	0.213 6
电机中心距离（俯仰方向）/m	0.175 8
无人机尺寸/m	0.363×0.403×0.139
三轴转动惯量J/ （kg·m²）	diag（0.01， 0.0082， 0.0148）×10^-2
旋翼转动惯量J_RP/ （kg·m²）	1.287×10^-4
电机常参数C_R/（rad·min^-1）/%	15 873
电机常参数ϖ_b/（rad·min^-1）	1 711

A binocular vision⁃based autonomous aerial docking system design for UAVs

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参数	数值
起飞重量/g	87
最长飞行时间/min	13
最大水平飞行速度/（km·h^-1）	28.8
相机输入图像尺寸	640×480
电池类型	LiPo
电池容量/（mA·h）	1 100

参数	数值
总重量/kg	1.121
转动惯量/（kg·m²）	［0.01， 0.0082， 0.0148］×10^-2
电池参数	4s 14.8V LiPo （3 700 mA·h）
飞机尺寸/m	0.363×0.403×0.139
机载传感器	2个六自由度IMU（含陀螺仪和加速度计）、1个光流传感器、1个高度传感器

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