基于级联卷积神经网络的机器人平面抓取位姿快速检测

针对任意姿态的未知不规则物体，提出一种基于级联卷积神经网络的机器人平面抓取位姿快速检测方法．建立了一种位置-姿态由粗到细的级联式两阶段卷积神经网络模型，利用迁移学习机制在小规模数据集上训练模型，以R-FCN（基于区域的全卷积网络）模型为基础提取抓取位置候选框进行筛选及角度粗估计，并针对以往方法在姿态检测上的精度不足，提出一种Angle-Net模型来精细估计抓取角度．在Cornell数据集上的测试及机器人在线抓取实验结果表明，该方法能够对任意姿态、不同形状的不规则物体快速计算最优抓取点及姿态，其识别准确性和快速性相比以往方法有所提高，鲁棒性和稳定性强，且能够泛化适应未训练过的新物体．

Fast Planar Grasp Pose Detection for Robot Based on Cascaded Deep Convolutional Neural Networks

A fast planar grasp pose detection method for robot based on cascaded convolutional neural networks is proposed to detect the pose for the unknown irregular objects with arbitrary poses. A cascaded two-stage convolution neural network model based on from coarse-to fine-scale position-attitude is established. The transfer-learning mechanism is used to train the model on small scale data sets. The grasp position candidate bounding-boxes are extracted and the coarse angle is estimated based on the R-FCN (region-based fully convolutional network) model. Angle-Net is proposed to solve the low accuracy detection problem of the previous methods, which can estimate the grasp angles with higher accuracy. Validations on the Cornell dataset and online grasp experiments on the real robot indicate that the proposed method can fast calculate the optimal grasp point and attitude for irregular objects with any shape and pose, the detection accuracy and speed are improved compared with the previous methods, the robustness and stability are strong, and it can be generalized to adapt to new object untrained.