水下航行器视觉控制技术综述

高剑; 何耀祯; 陈依民; 张元旭; 杨旭博; 李宇丰; 张桢驰

doi:10.11993/j.issn.2096-3920.2023-0061

水下航行器视觉控制技术综述

doi: 10.11993/j.issn.2096-3920.2023-0061

西北工业大学, 陕西西安, 710072

基金项目: 国家自然科学基金项目资助(51979228, 52102469).

详细信息

作者简介:
高剑：高　剑(1979-),男,教授,主要研究方向为水下航行器控制技术

中图分类号: U674.941; U666.1
计量
- 文章访问数: 93
- HTML全文浏览量: 1
- PDF下载量: 31
- 被引次数: 0
出版历程
- 收稿日期: 2023-05-19
- 修回日期: 2023-07-03
- 录用日期: 2023-08-17
- 网络出版日期: 2023-12-18

Review of Visual Control Technologies for Underwater Vehicles

Northwestern Polytechnical University, Xi’an 710072, China

摘要

摘要: 视觉控制是通过视觉信息进行环境和自身状态感知的一种控制方式, 文中将该技术应用于水下航行器控制, 并对不同应用场景下的相关研究进展、难点与趋势进行分析。首先介绍水下航行器视觉控制技术发展现状与任务场景, 主要对水下图像增强、目标识别与位姿估计技术进行介绍, 并从水下视觉动力定位与目标跟踪、水下航行器对接及水下作业任务目标抓取等三个任务场景对水下航行器视觉控制技术发展现状进行总结和分析, 最后梳理了水下航行器视觉控制技术的难点与发展趋势。
- 水下航行器 /
- 水下视觉 /
- 视觉控制
Abstract: Visual control is a method of control that utilizes visual information for environmental and self-awareness. In this paper, this technology was applied to the control of underwater vehicles, and an analysis of relevant research progress, challenges, and trends in different application scenarios was presented. The current development and task scenarios of visual control for underwater vehicles were first introduced. The focus was on the introduction of underwater image enhancement, target recognition, and pose estimation technologies. A summary and analysis of the current development of visual control technology for underwater vehicles were then provided based on three task scenarios: underwater visual odometry and target tracking, underwater vehicle docking, and underwater operational tasks such as target grasping. Finally, the challenges and development trends of visual control for underwater vehicles were outlined.
- underwater vehicle /
- underwater vision /
- visual control

HTML全文

图 1 常见的水下航行器

Figure 1. Common Underwater Vehicles

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图 2 视觉与机械臂抓取协调控制试验场景

Figure 2. Experiment scene of coordinated control of vision and manipulator grasping

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图 3 水下图像增强与复原效果对比

Figure 3. Comparison of underwater visual image enhancement and restoration effects

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图 4 MODA算法的识别结果^[30]

Figure 4. The recognition results of the MODA algorithm^[30]

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图 5 FORSSEA航行器视觉动力定位^[51]

Figure 5. Visual Dynamic Positioning of FORSSEA Underwater Vehicle^[51]

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图 6 Porto大学水下航行器动力定位^[58]

Figure 6. Dynamic Positioning of Underwater Vehicle at Porto University^[58]

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图 7 西北工业大学水下航行器动力定位试验场景^[62]

Figure 7. Dynamic positioning scenario for underwater vehicles at Northwestern Polytechnical University^[62]

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图 8 水下航行器对接过程^[72]

Figure 8. Docking process of underwater vehicle^[72]

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图 9 中国科学院沈阳自动化研究所航行器水下对接装置

Figure 9. Underwater docking station of Shenyang Institute of Automation Chinese Academy of Sciences

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图 10 西北工业大学水池对接实验场景^[81]

Figure 10. Experimental scenario of water pool docking at Northwestern Polytechnical University^[81]

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图 11 水下航行器水池目标抓取场景

Figure 11. Underwater vehicle pool target grasping scene

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图 12 水下目标抓取的机器人视角

Figure 12. Robot Perspective of Underwater Target Grasping

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图 13 大连海事大学“海星”号UVMS^[94]

Figure 13. Dalian Maritime University "Starfish" UVMS^[94]

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