摘要
针对海参养殖领域传统人工盘点方法效率低、随机性强且覆盖率不足的问题,设计了一种可自主作业并能实时面积计算的水下观测机器人(ROV),旨在实现养殖区全覆盖扫描与精准生物量监测。首先,提出一种集视觉采集、运动控制、动态面积计算于一体的ROV系统框架;然后结合海参圈实际养殖场景设计基于PID的定高、定航的自主运动功能;接着依据运动学模型解算ROV位姿设计“几”字形路径规划算法,实现水下区域全覆盖,并依据相机标定参数动态计算相机扫描面积。实验结果显示,所设计的水下观测机器人垂直定位精度为±0.02 m,面积计算误差小于2.31%,密度估算误差小于5%。该ROV以传感-运动-计算闭环设计,解决了水下机器人自主作业与实时面积反馈问题,为海参养殖提供了全自动、高可靠性的生物量监测方案。
Aiming to overcome the inefficiency,randomness,and insufficient coverage of traditional manual inventory methods in sea cucumber aquaculture,an autonomous underwater observation robot(ROV)with real-time area calculation capabilities was developed.The system was designed to enable comprehensive scanning of breeding zones and accurate biomass estimation.Firstly,an integrated ROV framework was proposed,combining visual acquisition,motion control,and dynamic area computation.Secondly,based on the operational requirements of sea cucumber ponds,autonomous motion functions,including depth-holding and course-keeping were implemented by using PID control algorithms.The ROV pose was determined through kinematic modeling,and a“crisscross”path planning strategy was employed to ensure complete seabed coverage.Real-time scanning area estimation was performed using calibrated camera parameters to enhance measurement accuracy.Experimental validation in simulated and real aquaculture environments demonstrated that the ROV achieved a vertical positioning accuracy of±0.02 m,an area calculation error below 2.31%,and a density estimation error under 5%.Furthermore,the system exhibited robust performance under varying turbidity conditions,maintaining stable vision-based detection even in low-visibility scenarios.The closed-loop control architecture,integrating perception,navigation,and computation,effectively addressed the challenges of autonomous underwater operation and real-time monitoring.Compared with conventional methods,the proposed system significantly improved inventory efficiency while reducing labor costs and human error.Future work would focus on multi-ROV collaborative operation and machine learning-enhanced sea cucumber recognition for large-scale aquaculture applications.This research contributed a fully automated,high-precision monitoring solution for sustainable aquaculture management.
作者
王文良
刘晓阳
印长坤
赵和鸣
陈启俊
张家旭
张海光
李国栋
王魏
林远山
WANG Wenliang;LIU Xiaoyang;YIN Changkun;ZHAO Heming;CHEN Qijun;ZHANG Jiaxu;ZHANG Haiguang;LI Guodong;WANG Wei;LIN Yuanshan(School of Information Engineering,Dalian Ocean University,Dalian 116023,China;Dalian Key Laboratory of Smart Fisheries,Dalian 116023,China;Dalian Xinyulong Marine Biological Seed Industry Science and Technology Co.,Ltd.,Dalian 116007,China;Institute of Fisheries Machinery and Instrumentation,Chinese Academy of Fishery Sciences,Shanghai 200092,China;Key Laboratory of Facilities Fisheries,Ministry of Education,Dalian Ocean University,Dalian 116023,China)
出处
《农业机械学报》
北大核心
2025年第8期535-543,601,共10页
Transactions of the Chinese Society for Agricultural Machinery
基金
辽宁省属本科高校基本科研业务费专项资金项目(2024JBZDZ004)
2023中央财政对辽宁渔业补助项目
辽宁省重点研发计划项目(2023JH26/10200015)
辽宁省自然科学基金项目(2020-KF-12-09)
辽宁省教育厅基本科研项目(LJKZ0730、QL202016)
设施渔业教育部重点实验室开放课题(202219)
广西重点研发计划项目(桂科AB23075150)
辽宁省应用基础计划项目(2022JH2/101300187)
辽宁省科技计划联合计划项目(2024JH2/102600083)。
关键词
海参
水下观测机器人
区域覆盖
密度估计
相机标定
sea cucumber
underwater observation robots
regional coverage
density estimation
camera calibration
