In the field of numerical control machining, tool alignment technology is a key link to ensure machining accuracy and quality. Tool alignment refers to determining the correct position of the tool relative to the workpiece, and its accuracy directly affects the precision of part machining. With the development of precision machining technology, the research and application of cutting technology are increasingly valued. Tool alignment methods are mainly divided into two categories: contact and non-contact. The contact type tool alignment method relies on direct contact between the tool and the workpiece or tool alignment instrument to measure the position. Among them, the trial cutting method is a traditional contact type tool alignment method that determines the tool position through actual cutting, which is intuitive but inefficient. The contact type tool presetter uses specialized equipment to improve the accuracy and efficiency of tool presetting through contact measurement. The non-contact tool alignment method does not rely on physical contact, while the image method uses image recognition technology to determine the tool position, making it suitable for high-precision applications. The laser diffraction method and the laser direct method use laser technology for non-contact measurement. The laser diffraction method determines the position of the tool by analyzing the diffraction mode of the laser beam, while the laser direct method directly measures the distance between the laser and the tool. This article mainly introduces the classification of tool alignment, commonly used knife alignment methods and common tool alignment devices, as well as the development status of international tool alignment instrument products.

This paper proposes an underwater image enhancement technology based on an artificial lateral inhibition network (ALIN) generated in the compound eye of a bionic horseshoe crab robot (BHCR). The concept of a horizontal suppression network is applied to underwater image processing with the aim of achieving low energy consumption, high efficiency processing, and adaptability to limited computing resources. The lateral inhibition network has the effect of “enhancing the center and suppressing the surroundings”. In this paper, a pattern recognition algorithm is used to compare and analyze the images obtained by an artificial lateral inhibition network and eight main underwater enhancement algorithms (white balance, histogram equalization, multi-scale Retinex, and dark channel). Therefore, we can evaluate the application of the artificial lateral inhibition network in underwater image enhancement and the deficiency of the algorithm. The experimental results show that the ALIN plays an obvious role in enhancing the important information in underwater image processing technology. Compared with other algorithms, this algorithm can effectively improve the contrast between the highlight area and the shadow area in underwater image processing, solve the problem that the information of the characteristic points of the collected image is not prominent, and achieve the unique effect of suppressing the intensity of other pixel points without information. Finally, we conduct target recognition verification experiments to assess the ALIN’s performance in identifying targets underwater with the BHCR in static water environments. The experiments confirm that the BHCR can maneuver underwater using multiple degrees of freedom (MDOF) and successfully acquire underwater targets.

This paper presents a novel multifunctional intestinal robot that integrates capabilities for dual biopsy and drug release with animproved YOLOv8-DCP algorithm for enhanced polyp detection under endoscopic conditions. The proposed system addressesthe limitations of single-function robots by enabling multiple medical operations within a single treatment session. The YOLOv8-DCP algorithm, designed specifically for the complex intestinal environment, incorporates a convolution and attention fusionmodule (CAFM), a Dense Convolutional Feature Extraction Module (DC2F), and a P6 detection head. Experimental resultsdemonstrate significant improvements in detection accuracy, with the YOLOv8-DCP model outperforming basic models acrossall metrics, particularly in the detection of large polyps. This study offers a promising approach for the more accurate andefficient detection and treatment of intestinal polyps, contributing to the advancement of clinical applications in intestinalrobotics.