A system of robotic grasping with experience acquisition

Robotic grasping has played a fundamental role in the robotic manipulation, while grasping an unknown object is still a challenge. A successful grasp is largely determined by the object representation and the corresponding grasp planning strategy. With the help of RGBD camera, the point cloud of the object can be obtained conveniently. However, the large amount of point cloud is often unorganized with some inevitable noise. It may result in the geometry of the object imprecise and lead to some poor grasp planning. In this paper, a parametric model—superquadric is chosen to represent the shape of an object. We firstly recover the superquadric of an object from the raw point cloud in a single view with conjugate gradient method. Then a force-closure grasp planning strategy is applied to this object to obtain stable grasp configurations. Finally we store the grasp parameters as grasp experience in a grasp dataset which can be used for future grasping tasks. The performance of the proposed grasping system is represented both in simulation and actual experiment scenario successfully.概要创新点抓取操作是机器人操作的基础, 本文提出一种可获取经验信息的机器人抓取操作系统. 该系统由目标物表达, 抓取规划和经验获取三部分组成. 通过Kinect得到目标物初始点云数据, 而后用超二次曲面对目标物进行表达, 最后经过抓取规划得到力闭合抓取位姿, 于此同时将目标物参数与抓取参数以经验信息的形式存储于抓取经验库中, 为机械手抓取类似物体提供经验知识.

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