摘要
随着机械臂在越来越多的场合扮演着重要的角色,准确的抓取位置检测是整个机械臂系统顺利完成任务的关键,为此提出一种以整个图片为输入直接输出结果的端到端实时检测方案.物体的抓取点位置会影响到该物体的抓取角度,基于此给出了一种两阶段预测方案将这两个要素分开预测.首先,建立一个卷积神经网络预测物体的抓取点位置;然后,以抓取点位置为中心采集原图像中的一个方形区域.针对这一区域利用Canny算法以及Hough变换进行边缘提取和直线检测,并提出一种主方向提取算法,分析得到直线,进而确定物体的角度和抓取时平行夹持器张开的间距.抓取位置检测算法给出了基于RGB图像预测的较好准确率,神经网络与传统方法的结合使用也为以后的研究提供了参考.
Recently,robots have played big roles in more and more cases.An accurate grasp detection is a key component of a robot working process.An end-to-end method for robotic grasp detection in an RGB image containing objects is proposed in such a case,which takes the whole picture as input and gives the prediction result directly without using traditional sliding windows or region extraction.Obviously,different grasp points lead to different grasp orientations.The grasp detection method takes two steps.First,a convolutional neural network is trained to predict the positions of grasp points.Next,a square area with the preceding grasp point as the center is taken from the image,where the edges are extracted using the Canny edge detection and the lines are detected using Hough Transform.A principal-directiondetection algorithm is proposed to analyze these lines and detect grasp orientations and the distance between two parallel fingers.The method gives a better grasp detection and has an influence on computer vision using both deep learning and traditional algorithms.
作者
熊军林
赵铎
XIONG Junlin;ZHAO Duo(Department of Automation, University of Science and Technology of China, Hefei 230027,China)
基金
国家自然科学基金(61773357)资助.
关键词
抓取位置检测
卷积神经网络
边缘提取
直线提取
主方向提取
robot grasping detection
convolutional neural network
edge detection
line detection
principal-direction detection
