基于深度学习的视觉检测及抓取方法

孙先涛; 程伟; 陈文杰; 方笑晗; 陈伟海; 杨茵鸣

doi:10.13700/j.bh.1001-5965.2022.0130

基于深度学习的视觉检测及抓取方法

doi: 10.13700/j.bh.1001-5965.2022.0130

1.
安徽大学电气工程与自动化学院，合肥 230601
2.
北京航空航天大学自动化科学与电气工程学院，北京 100191

基金项目: 国家自然科学基金(52005001)

详细信息

通讯作者:
E-mail：whchen@buaa.edu.cn

中图分类号: TP242
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出版历程
- 收稿日期: 2022-03-08
- 录用日期: 2022-05-06
- 网络出版日期: 2022-05-26
- 整期出版日期: 2023-10-31

A visual detection and grasping method based on deep learning

1.
School of Electrical Engineering and Automation，Anhui University，Hefei 230601，China
2.
School of Automation Science and Electrical Engineering，Beihang University，Beijing 100191，China

Funds: National Natural Science Foundation of China (52005001)

More Information

Corresponding author: E-mail：whchen@buaa.edu.cn

摘要

摘要:
针对现有机器人抓取系统对硬件设备要求高、难以适应不同物体及抓取过程产生较大有害扭矩等问题，提出一种基于深度学习的视觉检测及抓取方法。采用通道注意力机制对YOLO-V3进行改进，增强网络对图像特征提取的能力，提升复杂环境中目标检测的效果，平均识别率较改进前增加0.32%。针对目前姿态估计角度存在离散性的问题，提出一种基于视觉几何组-16（VGG-16）主干网络嵌入最小面积外接矩形(MABR)算法，进行抓取位姿估计和角度优化。改进后的抓取角度与目标实际角度平均误差小于2.47°，大大降低两指机械手在抓取过程中对物体所额外施加的有害扭矩。利用UR5机械臂、气动两指机械手、Realsense D435相机及ATI-Mini45六维力传感器等设备搭建了一套视觉抓取系统，实验表明：所提方法可以有效地对不同物体进行抓取分类操作、对硬件的要求较低、并且将有害扭矩降低约75%，从而减小对物体的损害，具有很好的应用前景。
- 深度学习 /
- 神经网络 /
- 目标检测 /
- 姿态估计 /
- 机器人抓取
Abstract:
This paper proposes a deep learning based visual detection and grasping method to solve the problems of the existing robotic grasping systems, including high hardware costs, difficulty in adapting to different objects, and large harmful torques. The channel attention mechanism is used to enhance the ability of the network to extract image features, improving the effect of target detection in complex environments using the improved YOLO-V3. It is found that the average recognition rate is increased by 0.32% compared with that before the improvement. In addition, to address the discreteness of estimated orientation angles, an embedded minimum area bounding rectangle (MABR) algorithm based on VGG-16 backbone network is proposed to estimate and optimize the grasping position and orientation. The average error between the improved predicted grasping angle and the actual angle of the target is less than 2.47°, significantly reducing the additional harmful torque applied by the two-finger gripper to the object in the grasping process. This study then builds a visual grasping system, using a UR5 robotic arm, a pneumatic two-finger robotic gripper, a Realsense D435 camera, and an ATI-Mini45 six-axis force/torque sensor. Experimental results show that the proposed method can effectively grasp and classify objects, with low requirements for hardware. It reduces the harmful torque by about 75%, thereby reducing damage to grasped objects, and showing a great application prospect.
- deep learning /
- neural network /
- object detection /
- pose estimation /
- robotic grasping

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图 1 抓取系统图

Figure 1. Grasping system diagram

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图 2 YOLO-V3算法结构

Figure 2. Structure of YOLO-V3 algorithm

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图 3 通道注意力机制模块

Figure 3. Channel attention mechanism module

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图 4 五维抓取框图

Figure 4. Five-dimensional grasping frame diagram

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图 5 抓取位姿估计网络结构

Figure 5. Structure of grasping pose estimation

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图 6 角度识别原理图

Figure 6. Angle recognition schematic diagram

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图 7 目标检测类别图

Figure 7. Target detection category diagram

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图 8 目标检测结果示意图

Figure 8. Digram of target detection results

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图 9 抓取位姿估计结果

Figure 9. Grasping pose estimation results

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图 10 系统样机搭建

Figure 10. System prototype setup

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图 11 抓取实验

Figure 11. Grasping experiments

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图 12 抓取扭矩图

Figure 12. Grasping torsion diagram

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表 1 不同网络结构对比

Table 1. Comparison of different network structure

网络结构	准确率/%		运行时间/s
网络结构	cornell数据集	实验目标	运行时间/s
双层网络结构ResNet-50	91.30	87.11	0.932
单层网络结构ResNet-50	91.12	86.69	0.714
单层网络结构 VGG-16	90.89	87.19	0.286

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表 2 位姿估计结果

Table 2. Pose estimation results

目标	目标抓取点(u, v)/像素		目标抓取角度/(°)		目标实际角度/(°)
目标	改进前	改进后	改进前	改进后	目标实际角度/(°)
control board	(107, 112.2)	(107, 112.2)	100	124	123
hammer	(92.3, 109.3)	(92.3, 109.3)	30	18	23
shovel	(111.3, 108.5)	(111.3, 108.5)	50	62	59
wrench	(87.5, 132.5)	(87.5, 132.5)	40	46	44
scissors	(104.5, 118.3)	(104.5, 118.3)	50	53	54
pliers	(88.1, 114.5)	(88.1, 114.5)	40	48	52
umbrella	(88.5, 98.4)	(88.5, 98.4)	30	35	35
weight counter	(100.5, 136.2)	(100.5, 136.2)	90	135	127
stapler	(106.9, 104.7)	(106.9, 104.7)	30	46	48
solid glue	(98.2, 120.9)	(98.2, 120.9)	40	45	45
screwdriver	(83.6, 110.2)	(83.6, 110.2)	130	161	162
sponge	(84.6, 118.7)	(84.6, 118.7)	180	13	14

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表 3 抓取实验数据

Table 3. Experimental data of grasping

编号	目标抓取点(x, y, z)/mm		目标抓取角度/(°)		目标实际角度/(°)	抓取扭矩/（N·mm）
编号	改进前	改进后	改进前	改进后	目标实际角度/(°)	改进前	改进后
实验1	(153.41, −675.29, 102.35)	(153.41, −675.29, 102.35)	50	52	53	4.0	2.3
实验2	(13122, −603.70, 99.16)	(13122, −603.70, 99.16)	50	58	58	9.5	0.3
实验3	(161.96, −558.44, 102.71)	(161.96, −558.44, 102.71)	140	157	156	15.0	2.6
实验4	(111.15, −574.50, 98.79)	(111.15, −574.50, 98.79)	10	21	19	8.0	5.0
实验5	(114.63, −732.19, 96.96)	(114.63, −732.19, 96.96)	30	39	47	19.0	11.0
实验6	(102.68, −657.68, 100.41)	(102.68, −657.68, 100.41)	40	51	50	10.6	2.5
实验7	(127.41, −675.63, 100.53)	(127.41, −675.63, 100.53)	40	46	45	4.0	1.5
实验8	(155.39, −597.67, 105.50)	(155.39, −597.67, 105.50)	50	53	55	8.0	3.8
实验9	(176.65, −690.90, 103.57)	(176.65, −690.90, 103.57)	90	111	113	17.5	4.0
实验10	(131.77, −739.27, 100.34)	(131.77, −739.27, 100.34)	100	112	112	12.5	0
实验11	(194.20, −687.68, 101.49)	(194.20, −687.68, 101.49)	30	36	35	5.0	2.5
实验12	(127.47, −590.49, 100.38)	(127.47, −590.49, 100.38)	30	63	63	25.0	0

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