This study addresses the challenges associated with incomplete or missing information in obstacle detection methods that employ a single sensor. Additionally, it tackles the issue of motion distortion in LiDAR point cloud data during synchronization and mapping in complex environments. The research introduces two significant contributions. Firstly, a novel obstacle detection method, named the point-map fusion (PMF) algorithm, was proposed. This method integrates point cloud data from the LiDAR, camera, and odometer, along with local grid maps. The PMF algorithm consists of two components: the point-fusion (PF) algorithm, which combines LiDAR point cloud data and camera laser-like point cloud data through a point cloud library (PCL) format conversion and concatenation, and selects the most proximate point cloud to the quadruped robot dog as the valid data; and the map-fusion (MF) algorithm, which incorporates local grid maps acquired using the Gmapping and OctoMap algorithms, leveraging Bayesian estimation theory. The local grid maps obtained by the Gmapping and OctoMap algorithms are denoted as map A and map B, respectively. This sophisticated methodology enables seamless map fusion, which significantly enhances the precision and reliability of the approach. Secondly, a motion distortion removal (MDR) method for LiDAR point cloud data based on odometer readings was proposed. The MDR method utilizes legged odometer data for linear data interpolation of the original distorted LiDAR point cloud data, facilitating the determination of the corresponding pose of the quadruped robot dog. Subsequently, the LiDAR point cloud data are then transformed to the quadruped robot dog coordinate system, efficiently mitigating motion distortion. Experimental results demonstrated that the proposed PMF algorithm achieved a 50% improvement in success rate compared to using only LiDAR or the PF algorithm in isolation, while the MDR algorithm enhanced mapping accuracy by 45.9% when motion distortion was taken into account. The effectiveness of the proposed methods was confirmed through rigorous experimentation.

The manufacturing of precise surface microstructures with low cost is needed for surface texturing-based surface engineering. In this paper, a device for the fabrication of surface microgroove texture on stainless steel based on ultrasonic impact peening (UIP) is proposed and investigated. First, the principle of applying the UIP into the fabrication of surface texture is analytically described. Then, the design of the UIP device, particularly the design of functional systems and mechanical structures, is carried out. Next, a UIP experimental device is built, and is further applied to fabricate microgroove textures on 316L stainless steel. The subsequent experimental characterization of microgroove morphology demonstrates the feasibility of the designed UIP device for the fabrication of stainless steel surface texture.

The dynamic model of magnetic dynamic absorber-rotor system is established. The equations for the equivalent stiffness and damping of the magnetic dynamic absorber are derived on the basis of the system dynamic model and operation principle of tunable dynamic absorber. Because the equivalent stiffness can be adjusted according to excitation frequency, so the rotor vibration can be controlled with the magnetic dynamic absorber in wide frequency band. The vibration absorption performance of the magnetic dynamic absorber is analyzed by means of simulation method. The results indicate that the tunable magnetic dynamic absorber has better absorption performance when a proper controller parameter k_d is selected. And the sine excitation response and step response of magnetic dynamic absorber-rotor system are also investigated at running speed. With considering system stability, settling time, overshoot and air gap between the rotor and the magnetic dynamic absorber, the controller parameter k_d is determined, and the unbalance vibration of rotor is reduced by 90% when k_d = 0.01.

Under the wave of digitalization and intelligence, smart education is becoming an important force to promote educational modernization, promote educational equity and improve educational quality. This manuscript elucidates the principle, features, and current progress of intelligent learning, with a particular emphasis on the examination of digital education strategies from different nations. From multiple dimensions, the differences between traditional education and smart education are compared and analyzed. The analysis examines the specific progression of intelligent education across different nations, emphasizing the fusion of educational and technological fields, the convergence between education and the industrial sector, and the tripartite integration of industry, academia, and research, along with the challenges. The focus of this manuscript is on pioneering integration strategies through case-based teaching, the joint educational framework of industry-academic convergence, the development of a multifaceted assessment approach, and the new ‘teacher-machine-student’ teaching model. The manuscript also discusses the future development trend of smart education. Research shows that the deep integration of smart education requires multi-party collaboration, continuous innovation of teaching models, further improvement of the evaluation system, and the fostering of creative individuals equipped for the demands of forthcoming societal challenges.