3D objects with features spanning from microscale to macroscale have various applications. However, the fabrication of such objects presents challenges to additive manufacturing (AM) due to the tradeoffs among manufacturable feature resolution, maximum build area, and printing speed. This paper presents a projection‐based AM process called hopping light vat photopolymerization (HL‐VPP) to address this critical barrier. The key idea of HL‐VPP is to synchronize linear scanning projection with a galvo mirror's rotation. The projector moves continuously at a constant speed while periodically rotating a one‐axis galvo mirror to compensate for the projector's linear movement so synchronized hopping motion can be achieved. By this means, HL‐VPP can simultaneously achieve large‐area (over 200 mm), fast‐speed (scanning speed of 13.5 mm s‐1), and high‐resolution (10 µm pixel size) fabrication. The distinguishing characteristic of HL‐VPP is that it allows for hundreds of times lower refresh rates without motion blur. Thus, HL‐VPP decouples the fabrication efficiency limit imposed by the refresh rate and will enable super‐fast curing in the future. This work will significantly advance VPP's use in applications that require macroscale part size with microscale features. The process has been verified by fabricating multiple multiscale objects, including microgrids and biomimetic structures.

In real world, due to the existence of floating particles such as smoke and dust in the atmosphere, images taken by camera are susceptible to different levels of blurring, low contrast, color distortion and visual degradation, which would be amplified when enlarging the image resolution. Therefore, it is a new trend to join the image dehazing and image super-resolution tasks. To generate sharp highresolution images from low-resolution images with severe haze, a common way is to connect the dehazing network and the super-resolution network in series. However, two-stage joint approach easily introduces blurring artifacts and is time-consuming. In addition, although there are a few existing one-stage methods, their training modes are relatively complicated and the restoration effects on some texture details are still blurry. In this paper, we focus on exploring one-stage joint model and propose a back-projection network based on shared source attention fusion (BPSAF), which forms a closed frame through back-projection mechanism. BPSAF can remove non-uniform haze and extend the resolution simultaneously. Specifically, a shared source attention fusion (SAF) module is presented to fuse high-frequency information of different level features using shared source skip connections more effectively, which filters out abundant thin haze and low-frequency information from merged images. To enhance the definition of restored images, a feedback error correction module based on error attention mechanism (FEC-EA) is designed to further correct distorted texture details by eliminating the feedback error from initial super-resolution result to the input hazy image. Experimental results demonstrate that our back-projection framework is superior to other existing methods in terms of quantitative indicators and visual quality.

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This article mainly designs an efficient fruit picking robot that combines rigidity and flexibility. The design of the robot adopts a mechanical arm with 5 degrees of freedom for the mecanum wheel. The mechanical arm is equipped with a gripper made using artificial muscle technology. The ZED2 binocular vision system is used to obtain spatial coordinates to realize the positioning and positioning of the mechanical arm. Video image acquisition. In order to realize the automatic recognition and picking of strawberries, this paper creates a strawberry training set, and uses the yolov3 algorithm to train the data and obtains a strawberry recognition model, which has a recall rate of larger strawberries and immature strawberries and the precision rate reached 90%, and the detection precision and precision rate of small target strawberries reached more than 80%. It can Meet the requirements of picking in real life.