Robotic Objects Detection and Grasping in Clutter Based on Cascaded Deep Convolutional Neural Network

The complex and changeable robotic operating environment will often cause the low success rate or failure of the robot grasping. This article proposes a grasp pose detection method based on the cascaded convolutional neural network, which can be applied to grasp unknown irregular objects under unstructured environment. The grasping feature and the grasp position candidate bounding-boxes of objects are extracted by Mask-RCNN. To guarantee the generalization and improve the detection rate, grasp angle estimation network Y-Net is proposed to accurately obtain the grasp angle. To solve the problem of insufficient accuracy of grasping position, grasping feasibility evaluation network Q-Net is proposed for acquiring the grasp quality distribution. Finally, the optimal grasp posture is obtained for the robotic object grasping task in cluttered scenes. Experiments are validated in Cornell datasets, Jacquard datasets, and real environments, respectively. The experimental results show that the proposed method can quickly calculate the robot posture for irregular objects with random poses and different shapes. Compared to the previous methods, it has considerable improvement in grasp accuracy and speed. The method can be applied to object grasping scenarios in cluttered scenes and has strong stability and robustness.