Abstract: To enhance the autonomy of underwater operations in marine and ship engineering, this paper proposes a method for object grasping based on an MB-Grasp model. First, an RGB image of the target is captured by a binocular camera mounted on the operation platform, and a disparity depth map is generated using a semi-global stereo matching (SGM) algorithm. The RGB and depth images are then fed into the MB-Grasp network to estimate the target's position, category, and grasp angle. Subsequently, the target's pose in the robotic arm base coordinate system is derived through coordinate transformation. Path planning is performed using the rapidly-exploring random tree star (RRT*) algorithm, and inverse kinematics solves the joint angles for each path point. A smooth trajectory in joint space is generated via polynomial interpolation to drive the robotic arm to execute the grasp. An experimental platform simulating the underwater operation environment of a ship was constructed, and autonomous grasping of simulated cables and small components was successfully achieved. This study provides a feasible solution for autonomous underwater operations in ship underwater engineering.

Key words: underwater vehicle, manipulator systems, MB-Grasp, semi-global stereo matching (SGM) algorithm, rapidly-exploring random tree star (RRT*) algorithm, ship underwater operations

摘要： 为提升船舶与海洋工程水下作业的自主化水平,该文提出一种基于MB-Grasp模型的水下目标物抓取方法。首先通过搭载于作业平台的双目相机获取目标物RGB图像,再结合半全局立体匹配算法得到视差深度图;随后将RGB图像和深度图输入MB-Grasp网络,估计目标物的位置、种类及抓取角,并通过坐标变换计算其在机械臂基坐标系下的位姿;最终利用RRT*（rapidly-exploring random tree star）算法进行路径规划,通过逆运动学求解各路径点关节角,经多项式插值生成关节空间平滑轨迹,驱动机械臂完成抓取。该研究搭建了模拟船舶水下作业环境的实验平台,成功实现了对模拟缆线和小型部件等目标的自主抓取,为船舶水下自主作业提供了可行方案。

关键词: 水下机器人, 机械臂系统, MB-Grasp, 半全局立体匹配算法, RRT*算法, 船舶水下作业