基于水下机器人的海产品智能检测与自主抓取系统

针对水下机器人实现自主抓取过程中缺乏引导系统的问题，提出了一种依托水下机器人的海产品智能检测与自主抓取系统，用来解决水下目标的智能检测问题，并引导水下机器人进行海产品的自主抓取。将卷积神经网络检测算法应用到水下场景，利用水下图像数据集训练特定的网络模型DSOD检测海产品。建立短基线定位系统定位水下作业的机器人。通过分析相机成像坐标系与定位系统坐标系之间的关系，提出了一种计算海产品实际位置的坐标转换方法，计算海产品的实际位置。设计了一种基于反馈机制的多信号分析方法，引导机器人在水下移动并抓捕海产品。为了验证所提系统的有效性，搭建了一款水下抓捕机器人，并成功将所提算法应用到机器人，在真实海洋环境中进行海产品的自主抓取实验。 

Intelligent detection and autonomous capture system of seafood based on underwater robot

Currently, underwater robot faces the tough challenges of lacking intelligent detection and autonomous capture system to guide. Therefore, autonomous capture is hard to be achieved. Toward this end, this paper proposes an intelligent detection and autonomous capture system to achieve intelligent detection of marine target and guide the underwater robot to autonomously capture seafood. First, we employ convolutional neural network to perform object detection task in underwater scene and train the DSOD with underwater dataset to accurately detect marine objects. What's more, the short baseline positioning system is built to locate the underwater robot. To calculate the position of the object relative to robot, this paper proposes a coordinate transforming method to transform the target's location from camera coordinates system to underwater positioning coordinates. Furthermore, this paper designs a multi-signal analysis method based on feedback mechanism to command the robot to move ahead to the seafood until grasping them. To verify the effectiveness of the system, we develop an underwater picking robot and successfully apply the proposed methods to the robot to autonomously detect and capture the marine object.