Improved RRT* Algorithm for Disinfecting Robot Path Planning.

In this paper, an improved APF-GFARRT* (artificial potential field-guided fuzzy adaptive rapidly exploring random trees) algorithm based on APF (artificial potential field) guided sampling and fuzzy adaptive expansion is proposed to solve the problems of weak orientation and low search success rate when randomly expanding nodes using the RRT (rapidly exploring random trees) algorithm for disinfecting robots in the dense environment of disinfection operation. Considering the inherent randomness of tree growth in the RRT* algorithm, a combination of APF with RRT* is introduced to enhance the purposefulness of the sampling process. In addition, in the context of RRT* facing dense and restricted environments such as narrow passages, adaptive step-size adjustment is implemented using fuzzy control. It accelerates the algorithm's convergence and improves search efficiency in a specific area. The proposed algorithm is validated and analyzed in a specialized environment designed in MATLAB, and comparisons are made with existing path planning algorithms, including RRT, RRT*, and APF-RRT*. Experimental results show the excellent exploration speed of the improved algorithm, reducing the average initial path search time by about 46.52% compared to the other three algorithms. In addition, the improved algorithm exhibits faster convergence, significantly reducing the average iteration count and the average final path cost by about 10.01%. The algorithm's enhanced adaptability in unique environments is particularly noteworthy, increasing the chances of successfully finding paths and generating more rational and smoother paths than other algorithms. Experimental results validate the proposed algorithm as a practical and feasible solution for similar problems.

Research on the Preparation and Application of Fixed-Abrasive Tools Based on Solid-Phase Reactions for Sapphire Wafer Lapping and Polishing.

Single-crystal sapphire specimen (α-Al2O3) have been widely applied in the semiconductor industry, microelectronics, and so on. In order to shorten the production time and improve the processing efficiency of sapphire processing, an integrated fixed-abrasive tool (FAT) based on solid-phase reactions is proposed in this article. The optimal FAT composition is determined using a preliminary experiment and orthogonal experiments. The mass fraction of the abrasives is chosen as 55 wt%, and the mass ratio of SiO2/Cr2O3 is 2. Surface roughness Ra decreased from 580.4 ± 52.7 nm to 8.1 ± 0.7 nm after 150 min, and the average material removal rate was 14.3 ± 1.2 nm/min using the prepared FAT. Furthermore, FAT processing combined with chemical mechanical polishing (CMP) was shortened by 1.5 h compared to the traditional sapphire production process in obtaining undamaged sapphire surfaces with a roughness of Ra < 0.4 nm, which may have the potential to take the place of the fine lapping and rough polishing process.

Method for Denoising the Vibration Signal of Rotating Machinery through VMD and MODWPT.

The vibration signals from rotating machinery are constantly mixed with other noises during the acquisition process, which has a negative impact on the accuracy of signal feature extraction. For vibration signals from rotating machinery, the conventional linear filtering-based denoising method is ineffective. To address this issue, this paper suggests an enhanced signal denoising method based on maximum overlap discrete wavelet packet transform (MODWPT) and variational mode decomposition (VMD). VMD decomposes the vibration signal of rotating machinery to produce a set of intrinsic mode functions (IMFs). By computing the composite weighted entropy (CWE), the phantom IMF component is then removed. In the end, the sensitive component is obtained by computing the value of the degree of difference (DID) after the high-frequency noise component has been decomposed through MODWPT. The denoised signal reconstructs the signal's intrinsic characteristics as well as the denoised high-frequency IMF component. This technique was used to analyze the simulated and real-world signals of gear faults and it was compared to wavelet threshold denoising (WTD), empirical mode decomposition reconstruction denoising (EMD-RD), and ensemble empirical mode decomposition wavelet threshold denoising (EEMD-WTD). The outcomes demonstrate that this method can accurately extract the signal feature information while filtering out the noise components in the signal.

Ferromagnetic Flexible Electronics for Brain-Wide Selective Neural Recording.

Flexible microelectronics capable of straightforward implantation, remotely controlled navigation, and stable long-term recording hold great promise in diverse medical applications, particularly in deciphering complex functions of neural circuits in the brain. Existing flexible electronics, however, are often limited in bending and buckling during implantation, and unable to access a large brain region. Here, an injectable class of electronics with stable recording, omnidirectional steering, and precise navigating capabilities based on magnetic actuation is presented. After simple transcriptional injection, the rigid coatings are biodegraded quickly and the bundles of magnetic-nanoparticles-coated microelectrodes become separated, ultra-flexible, and magnetic actuated for further minimally invasive three-dimensional interpenetration in the brain. As proof of concept, this paradigm-shifting approach is demonstrated for selective and multiplexed neural activities recording across distant regions in the deep rodent brains. Coupling with optogenetic neural stimulation, the unique capabilities of this platform in electrophysiological readouts of projection dynamics in vivo are also demonstrated. The ability of these miniaturized, remotely controllable, and biocompatible ferromagnetic flexible electronics to afford minimally invasive manipulations in the soft tissues of the mammalian brain foreshadows applications in other organ systems, with great potential for broad utility in biomedical science and engineering.

Magneto-Responsive Shutter for On-Demand Droplet Manipulation.

Magnetically responsive structured surfaces enabling multifunctional droplet manipulation are of significant interest in both scientific and engineering research. To realize magnetic actuation, current strategies generally employ well-designed microarrays of high-aspect-ratio structure components (e.g., microcilia, micropillars, and microplates) with incorporated magnetism to allow reversible bending deformation driven by magnets. However, such magneto-responsive microarray surfaces suffer from highly restricted deformation range and poor control precision under magnetic field, restraining their droplet manipulation capability. Herein, a novel magneto-responsive shutter (MRS) design composed of arrayed microblades connected to a frame is developed for on-demand droplet manipulation. The microblades can perform two dynamical transformation operations, including reversible swing and rotation, and significantly, the transformation can be precisely controlled over a large rotation range with the highest rotation angle up to 3960°. Functionalized MRSs based on the above design, including Janus-MRS, superhydrophobic MRS (SHP-MRS) and lubricant infused slippery MRS (LIS-MRS), can realize a wide range of droplet manipulations, ranging from switchable wettability, directional droplet bounce, droplet distribution, and droplet merging, to continuous droplet transport along either straight or curved paths. MRS provides a new paradigm of using swing/rotation topographic transformation to replace conventional bending deformation for highly efficient and on-demand multimode droplet manipulation under magnetic actuation.

Laser Direct Structuring of Bioinspired Spine with Backward Microbarbs and Hierarchical Microchannels for Ultrafast Water Transport and Efficient Fog Harvesting.

Achieving effective dropwise capture and ultrafast water transport is essential for fog harvesting. In nature, cactus uses the conical spine with microbarbs to effectively capture fog, while Sarracenia utilizes the trichome with hierarchical microchannels to quickly transport water. Herein, we combined their advantages to present a novel configuration, a spine with barbs and hierarchical channels (SBHC), for simultaneous ultrafast water transport and high-efficient fog harvesting. This bioinspired SBHC exhibited the fastest water transport ability and the highest fog harvesting efficiency in comparison with the spine with hierarchical channels (SHCs), the spine with barbs and grooves (SBG), and the spine with barbs (SB). Based on the fundamental SBHC unit, we further designed and fabricated a two-dimensional (2D) spider-web-like fog collector and a three-dimensional (3D) cactus-like fog collector using direct laser structuring and origami techniques. The 2D spider-web and 3D cactus-like fog collectors showed high-efficient fog collection capacity. We envision that this fundamental understanding and rational design strategy can be applied in fog harvesting, heat transfer, liquid manipulation, and microfluidics.

A Method for Broccoli Seedling Recognition in Natural Environment Based on Binocular Stereo Vision and Gaussian Mixture Model.

Illumination in the natural environment is uncontrollable, and the field background is complex and changeable which all leads to the poor quality of broccoli seedling images. The colors of weeds and broccoli seedlings are close, especially under weedy conditions. The factors above have a large influence on the stability, velocity and accuracy of broccoli seedling recognition based on traditional 2D image processing technologies. The broccoli seedlings are higher than the soil background and weeds in height due to the growth advantage of transplanted crops. A method of broccoli seedling recognition in natural environments based on Binocular Stereo Vision and a Gaussian Mixture Model is proposed in this paper. Firstly, binocular images of broccoli seedlings were obtained by an integrated, portable and low-cost binocular camera. Then left and right images were rectified, and a disparity map of the rectified images was obtained by the Semi-Global Matching (SGM) algorithm. The original 3D dense point cloud was reconstructed using the disparity map and left camera internal parameters. To reduce the operation time, a non-uniform grid sample method was used for the sparse point cloud. After that, the Gaussian Mixture Model (GMM) cluster was exploited and the broccoli seedling points were recognized from the sparse point cloud. An outlier filtering algorithm based on k-nearest neighbors (KNN) was applied to remove the discrete points along with the recognized broccoli seedling points. Finally, an ideal point cloud of broccoli seedlings can be obtained, and the broccoli seedlings recognized. The experimental results show that the Semi-Global Matching (SGM) algorithm can meet the matching requirements of broccoli images in the natural environment, and the average operation time of SGM is 138 ms. The SGM algorithm is superior to the Sum of Absolute Differences (SAD) algorithm and Sum of Squared Differences (SSD) algorithms. The recognition results of Gaussian Mixture Model (GMM) outperforms K-means and Fuzzy c-means with the average running time of 51 ms. To process a pair of images with the resolution of 640×480, the total running time of the proposed method is 578 ms, and the correct recognition rate is 97.98% of 247 pairs of images. The average value of sensitivity is 85.91%. The average percentage of the theoretical envelope box volume to the measured envelope box volume is 95.66%. The method can provide a low-cost, real-time and high-accuracy solution for crop recognition in natural environment.