Grasping of unknown objects via curvature maximization using active vision
Grasping of unknown objects via curvature maximization using active vision
复制标题
使用主动视觉通过曲率最大化抓取未知物体
DOI：
10.1109/iros.2011.6094686
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
P. Jonker
中科院分区：
文献类型：
--
作者：
B. Çalli;M. Wisse;P. Jonker
抓取未知物体对于在非结构化环境中操作的机器人来说是至关重要的。在本文中，我们提出了一种新的抓取算法，使用主动视觉为基础。该算法使用的曲率信息获得的轮廓的对象。通过最大化曲率值，更新机器人的姿态，并实现合适的抓取配置。该算法与现有方法相比具有一定的优势：它不需要提取对象的3D模型，并且不依赖于离线获得的任何知识库。这导致在3D中更快且仍然可靠地抓取目标对象。通过仿真和实验验证了该算法的性能，并取得了成功的结果。
Grasping unknown objects is a crucial necessity for robots that operate in an unstructured environment. In this paper, we propose a novel grasping algorithm that uses active vision as basis. The algorithm uses the curvature information obtained from the silhouette of the object. By maximizing the curvature value, the pose of the robot is updated and a suitable grasping configuration is achieved. The algorithm has certain advantages over the existing methods: It does not require a 3D model of the object to be extracted, and it does not rely on any knowledge base obtained offline. This leads to a faster and still reliable grasping of the target object in 3D. The performance of the algorithm is examined by simulations and experiments, and successful results are obtained.