基于级联式Faster RCNN的三维目标最优抓取方法研究

机器人在三维目标识别和最优抓取方面的难点在于复杂的背景环境以及目标物体形状不规则,且要求机器人像人一样在识别不同三维目标的同时要确定该目标的最佳抓取部位的位姿。提出一种基于级联式模型的深度学习方法来识别目标物体及其最优抓取位姿。第1级提出了改进的Faster RCNN模型,该模型能识别成像小的目标物体,并能准确对其进行定位;第2级的Faster RCNN模型在前一级确定的目标物体上寻找该目标物体的最优抓取位姿,实现机器人的最优抓取。实验表明该方法能快速且准确地找到目标物体并确定其最优抓取位姿。

Research on 3D object optimal grasping method based on cascaded Faster RCNN

The difficulty of robot in 3D object recognition and optimal grasping lies in the complex background environment and the irregular shape of the target object. It requires the robot to determine the position and pose of the optimal grasping part of the target while recognizing different 3D targets like human. One kind of deep learning method based on the cascaded faster region based convolutional neural networks (RCNN) model is proposed to identify the target object and its optimal grasping pose. The improved Faster RCNN model is proposed at the first level, which can recognize small target objects and accurately locate them in the image. Then, a faster RCNN model at the second level is designed to find the optimal grasping pose of the target object recognized by the previous level to realize the optimal grasping of the robot. Experimental results show that the method proposed in this paper can find the object accurately and determine its optimal grasping pose.