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The positional information of objects is crucial to enable robots to perform grasping and pushing manipulations in clutter. To effectively perform grasping and pushing manipulations, robots need to perceive the position information of objects, including the coordinates and spatial relationship between objects (e.g., proximity, adjacency). The authors propose an end‐to‐end position‐aware deep Q‐learning framework to achieve efficient collaborative pushing and grasping in clutter. Specifically, a pair of conjugate pushing and grasping attention modules are proposed to capture the position information of objects and generate high‐quality affordance maps of operating positions with features of pushing and grasping operations. In addition, the authors propose an object isolation metric and clutter metric based on instance segmentation to measure the spatial relationships between objects in cluttered environments. To further enhance the perception capacity of position information of the objects, the authors associate the change in the object isolation metric and clutter metric in cluttered environment before and after performing the action with reward function. A series of experiments are carried out in simulation and real‐world which indicate that the method improves sample efficiency, task completion rate, grasping success rate and action efficiency compared to state‐of‐the‐art end‐to‐end methods. Noted that the authors' system can be robustly applied to real‐world use and extended to novel objects. Supplementary material is available at https://youtu.be/NhG\_k5v3NnM}{https://youtu.be/NhG\_k5v3NnM. [ABSTRACT FROM AUTHOR]

Non‐destructive detection of wire bonding defects in integrated circuits (IC) is critical for ensuring product quality after packaging. Image‐processing‐based methods do not provide a detailed evaluation of the three‐dimensional defects of the bonding wire. Therefore, a method of 3D reconstruction and pattern recognition of wire defects based on stereo vision, which can achieve non‐destructive detection of bonding wire defects is proposed. The contour features of bonding wires and other electronic components in the depth image is analysed to complete the 3D reconstruction of the bonding wires. Especially to filter the noisy point cloud and obtain an accurate point cloud of the bonding wire surface, a point cloud segmentation method based on spatial surface feature detection (SFD) was proposed. SFD can extract more distinct features from the bonding wire surface during the point cloud segmentation process. Furthermore, in the defect detection process, a directional discretisation descriptor with multiple local normal vectors is designed for defect pattern recognition of bonding wires. The descriptor combines local and global features of wire and can describe the spatial variation trends and structural features of wires. The experimental results show that the method can complete the 3D reconstruction and defect pattern recognition of bonding wires, and the average accuracy of defect recognition is 96.47%, which meets the production requirements of bonding wire defect detection. [ABSTRACT FROM AUTHOR]

Accurately identifying defect patterns in wafer maps can help engineers find abnormal failure factors in production lines. During the wafer testing stage, deep learning methods are widely used in wafer defect detection due to their powerful feature extraction capabilities. However, most of the current wafer defect patterns classification models have high complexity and slow detection speed, which are difficult to apply in the actual wafer production process. In addition, there is a data imbalance in the wafer dataset that seriously affects the training results of the model. To reduce the complexity of the deep model without affecting the wafer feature expression, this paper adjusts the structure of the dense block in the PeleeNet network and proposes a lightweight network WM‐PeleeNet based on the PeleeNet module. In addition, to reduce the impact of data imbalance on model training, this paper proposes a wafer data augmentation method based on a convolutional autoencoder by adding random Gaussian noise to the hidden layer. The method proposed in this paper has an average accuracy of 95.4% on the WM‐811K wafer dataset with only 173.643 KB of the parameters and 316.194 M of FLOPs, and takes only 22.99 s to detect 1000 wafer pictures. Compared with the original PeleeNet network without optimization, the number of parameters and FLOPs are reduced by 92.68% and 58.85%, respectively. Data augmentation on the minority class wafer map improves the average classification accuracy by 1.8% on the WM‐811K dataset. At the same time, the recognition accuracy of minority classes such as Scratch pattern and Donut pattern are significantly improved. [ABSTRACT FROM AUTHOR]

In this paper, a micro SLAM system based on ORB features for RGB-D cameras has been proposed. With only a RGB-D sensor, this method can be applied in small environment for localization and mapping. Furthermore, the task of 3D reconstruction can also be accomplished by using the approach. The pose graph based on Bundle Adjustment is adopted for reducing the estimation error. In order to further speed up computing to meet the requirement of real-time, we have proposed the piecewise optimization strategy. The approach is evaluated on public benchmark datasets. Compared with several state-of-the-art scheme, this method has proven to work well in these environments.