The instability issue of Pb-free Sn-based perovskite is one of the biggest challenges for its application in optoelectronic devices. Herein, a structural regulation strategy is demonstrated to regulate the geometric symmetry of formamidiniumtin iodide (FASnI 3) perovskite. Experimental and theoretical works show that the introduction of cesium cation (Cs⁺) could improve the geometric symmetry, suppress the oxidation of Sn²⁺, and enhance the thermodynamical structural stability of FASnI 3. As a result, the inverted planar Cs-doped FASnI 3-based perovskite solar cell (PSC) is shown to maintain 90% of its initial power-conversion efficiency (PCE) after 2000 h stored in N 2 , which is the best durability to date for 3D Sn-based PSCs. Most importantly, the air, thermal, and illumination stabilities of the PSCs are all improved after Cs doping. The PCE of the Cs-doped PSC shows a 63% increase compared to that of the control device (from 3.74% to 6.08%) due to the improved quality of the Cs-doped FASnI 3 film.
Grasp detection considiering the affiliations between grasps and their owner in object overlapping scenes is a necessary and challenging task for the practical use of the robotic grasping approach. In this paper, a robotic grasp detection algorithm named ROI-GD is proposed to provide a feasible solution to this problem based on Region of Interest (ROI), which is the region proposal for objects. ROI-GD uses features from ROIs to detect grasps instead of the whole scene. It has two stages: the first stage is to provide ROIs in the input image and the second-stage is the grasp detector based on ROI features. We also contribute a multi-object grasp dataset, which is much larger than Cornell Grasp Dataset, by labeling Visual Manipulation Relationship Dataset. Experimental results demonstrate that ROI-GD performs much better in object overlapping scenes and at the meantime, remains comparable with state-of-the-art grasp detection algorithms on Cornell Grasp Dataset and Jacquard Dataset. Robotic experiments demonstrate that ROI-GD can help robots grasp the target in single-object and multi-object scenes with the overall success rates of 92.5% and 83.8% respectively.
Robotic grasping is one of the most important fields in robotics and convolutional neural network (CNN) has made great progress in detecting robotic grasps. However, including multiple objects in one scene can invalidate the existing grasping detection algorithms based on CNN because of lacking of manipulation relationships among objects to guide the robot to grasp things in the right order. Therefore, the manipulation relationships are needed to help robot better grasp and manipulate objects. This paper presents a new CNN architecture called Visual Manipulation Relationship Network (VMRN) to help robot detect targets and predict the manipulation relationships in real time. To implement end-to-end training and meet real-time requirements in robot tasks, we propose the Object Pairing Pooling Layer (OP 2 L) to help to predict all manipulation relationships in one forward process. In order to train VMRN, we collect a dataset named Visual Manipulation Relationship Dataset (VMRD) consisting of 5185 images with more than 17000 object instances and the manipulation relationships between all possible pairs of objects in every image, which is labeled by the manipulation relationship tree. The experiment results show that the new network architecture can detect objects and predict manipulation relationships simultaneously and meet the real-time requirements in robot tasks.
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