轻量化机器人抓取位姿实时检测算法

Light-weight algorithm for real-time robotic grasp detection

针对机器人对形状、大小、种类变化不一的未知物体的抓取,提出轻量化的抓取位姿实时检测算法RTGN,以进一步提升抓取检测准确率及检测速度.设计多尺度空洞卷积模块,以构建轻量化的特征提取主干网络;设计混合注意力模块,以加强网络对重要抓取特征的关注;引入金字塔池化模块融合多层级特征,以提升网络对物体的抓取感知能力.在Cornell抓取数据集上进行测试,RTGN检测速度为142帧/s,在图像拆分和对象拆分上的检测准确率分别为98.26%和97.65%;在实际抓取环境下进行抓取实验,机器人对20类未知物体进行400次抓取,抓取成功率为96.0%.实验结果表明,RTGN的检测准确率和检测速度较现有方法有明显提升,对物体的位置和姿态变化具有较强的适应性,并且能够有效地泛化到形状、大小、种类等变化不一的未知物体的抓取检测中.

A light-weight,real-time approach named RTGN(real-time grasp net)was proposed to improve the accuracy and speed of robotic grasp detection for novel objects of diverse shapes,types and sizes.Firstly,a multiscale dilated convolution module was designed to construct a light-weight feature extraction backbone.Secondly,a mixed attention module was designed to help the network focus more on meaningful features.Finally,the pyramid pool module was deployed to fuse the multi-level features extracted by the network,thereby improving the capability of grasp perception to the object.On the Cornell grasping dataset,RTGN generated grasps at a speed of 142 frame per second and attained accuracy rates of 98.26%and 97.65%on image-wise and object-wise splits,respectively.In real-world robotic grasping experiments,RTGN obtained a success rate of 96.0%in 400 grasping attempts across 20 novel objects.Experimental results demonstrate that RTGN outperforms existing methods in both detection accuracy and detection speed.Furthermore,RTGN shows strong adaptability to variations in the position and pose of grasped objects,effectively generalizing to novel objects of diverse shapes,types and sizes.