Grasp planning with high-performance and efficiency in unstructured environments is a critical problem that intelligent robots need to solve urgently to complete picking tasks. To solve this problem, a quantized grasp quality generative neural network is proposed to generate pixel-level grasps. A light-weight convolutional neural network is first used to generate initial grasp configurations. Aiming at the degradation of grasping performance caused by the light-weight design, a decoupled grasp quality network is designed to generate a pixel-wise grasp quality. To improve the robustness and generalization of the model, an adaptive filtering method is proposed to filter the grasp configurations. Then, an ellipse fitting-based grasp pose optimization method is proposed to obtain the final grasp configuration. On the public Cornell dataset, the state-of-the-art grasp accuracy of 98.9% and the efficiency of 15 ms are achieved, with approximately 564× fewer parameters compared to comparable performance. In the grasping experiments on the robotic platform, grasp success rates of 99% and 93.4% can be achieved in single object and cluttered scenes, respectively. Finally, the model is deployed on an embedded artificial intelligence (AI) computing device to verify its practicality.

Grasping unseen cluttered objects is a challenging task for robots due to the uncertainty in the shape, size, and pose of objects to be grasped. In this article, we propose a novel method for instance segmentation of unseen objects, which is used to generate effective grasps for parallel grippers in cluttered scenes. Focusing on the grasp generation part, we propose a two-stage grasp generator to generate pixelwise grasps. In the first stage, a Taylor neural network (TNN) is used to design an instance segmentation model for segmenting unseen objects that explicitly learns second-order and above feature information from the input. In the second stage, a hierarchical grasp generation algorithm is employed for deducing the occlusion relationships between different instance masks. To reduce potential interference from nearby objects, grasps are estimated under different occlusion situations, and the optimal grasp is selected by combining depth information. We evaluate our method and achieve good segmentation results, outperforming state-of-the-art methods for unseen object instance segmentation. We also demonstrate a grasp success rate of 97.0% in cluttered object scenes.

As a representative technology of artificial intelligence, 3D reconstruction based on deep learning can be integrated into the edge computing framework to form an intelligent edge and then realize the intelligent processing of the edge. Recently, high-resolution representation of 3D objects using multiview decomposition (MVD) architecture is a fast reconstruction method for generating objects with realistic details from a single RGB image. The results of high-resolution 3D object reconstruction are related to two aspects. On the one hand, a low-resolution reconstruction network represents a good 3D object from a single RGB image. On the other hand, a high-resolution reconstruction network maximizes fine low-resolution 3D objects. To improve these two aspects and further enhance the high-resolution reconstruction capabilities of the 3D object generation network, we study and improve the low-resolution 3D generation network and the depth map superresolution network. Eventually, we get an improved multiview decomposition (IMVD) network. First, we use a 2D image encoder with multifeature fusion (MFF) to enhance the feature extraction capability of the model. Second, a 3D decoder using an effective subpixel convolutional neural network (3D ESPCN) improves the decoding speed in the decoding stage. Moreover, we design a multiresidual dense block (MRDB) to optimize the depth map superresolution network, which allows the model to capture more object details and reduce the model parameters by approximately 25% when the number of network layers is doubled. The experimental results show that the proposed IMVD is better than the original MVD in the 3D object superresolution experiment and the high-resolution 3D reconstruction experiment of a single image.

Segmenting unseen object instances in unstructured environments is an important skill for robots to perform grasping-related tasks, where the trade-off between efficiency and accuracy is an urgent challenge to be solved. In this work, we propose a fast unseen object instance segmentation (Fast UOIS) method that utilizes predicted center offsets of objects to compute the positions of local maxima and minima, which are then used for selecting initial seed points required by the mean-shift clustering algorithm. This clustering algorithm that adaptively generates seed points can quickly and accurately obtain instance masks of unseen objects. Accordingly, Fast UOIS first generates pixel-wise predictions of object classes and center offsets from synthetic depth images. Then, these predictions are used by the clustering algorithm to calculate initial seed points and to find possible object instances. Finally, the depth information corresponding to the filtered instance masks is fed into the grasp generation network to generate grasp poses. Benchmark experiments show that our method can be well transferred to the real world and can quickly generate sharp and accurate instance masks. Furthermore, we demonstrate that our method is capable of segmenting instance masks of unseen objects for robotic grasping.