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Robot pick-and-place for unknown objects is still a very challenging research topic. This paper proposes a multi-modal learning method for robot one-shot imitation of pick-and-place tasks. This method aims to enhance the generality of industrial robots while reducing the amount of data and training costs the one-shot imitation method relies on. The method first categorizes human demonstration videos into different tasks, and these tasks are classified into six types to symbolize as many types of pick-and-place tasks as possible. Second, the method generates multi-modal prompts and finally predicts the action of the robot and completes the symbolic pick-and-place task in industrial production. A carefully curated dataset is created to complement the method. The dataset consists of human demonstration videos and instance images focused on real-world scenes and industrial tasks, which fosters adaptable and efficient learning. Experimental results demonstrate favorable success rates and loss results both in simulation environments and real-world experiments, confirming its effectiveness and practicality.

The adaptability of a hexapod robot to complex terrain is highly dependent on its own posture, which directly affects its stability and flexibility. In order to adapt to a change in terrain, it is necessary to adjust posture in real time when walking. At the same time, external factors such as ground state and landing impact will also interfere with posture. Therefore, it is necessary to maintain balance after adjustment. This paper proposes a pose adjustment method utilizing joint angle control. It enhances robot stability, flexibility, and terrain adaptability through torso posture and center of gravity optimization, aiming to maintain balance. The strategy’s effectiveness was validated via Adams–Simulink co-simulation. Optimal position and posture adjustment for the torso was then implemented at the six-legged support stage after each step, employing inverse kinematics and a triangular gait. It is found that without pose adjustment, the direction deviation will accumulate and significantly deviate from the trajectory. The introduction of this adjustment can effectively correct the direction deviation and torso posture angle, increase the stability margin, ensure stable straight-line walking, and significantly reduce joint energy consumption. Crawling experiments with the physical prototype further validate the strategy. It rapidly counters instantaneous attitude fluctuations during leg alternation, maintaining a high stability margin and improving locomotion efficiency. Consequently, the robot achieves enhanced directional stability, overall stability, and energy efficiency when traversing terrain.

As a key component in the hydraulic brake system of automobiles, the brake joint directly affects the braking performance and driving safety of the vehicle. Therefore, improving the quality of brake joints is crucial. During the processing, due to the complexity of the welding material and welding process, the weld seam is prone to various defects such as cracks, pores, undercutting, and incomplete fusion, which can weaken the joint and even lead to product failure. Traditional weld seam detection methods include destructive testing and non-destructive testing; however, destructive testing has high costs and long cycles, and non-destructive testing, such as radiographic testing and ultrasonic testing, also have problems such as high consumable costs, slow detection speed, or high requirements for operator experience. In response to these challenges, this article proposes a defect detection and classification method for laser welding seams of automotive brake joints based on machine vision inspection technology. Laser-welded automotive brake joints are subjected to weld defect detection and classification, and image processing algorithms are optimized to improve the accuracy of detection and failure analysis by utilizing the high efficiency, low cost, flexibility, and automation advantages of machine vision technology. This article first analyzes the common types of weld defects in laser welding of automotive brake joints, including craters, holes, and nibbling, and explores the causes and characteristics of these defects. Then, an image processing algorithm suitable for laser welding of automotive brake joints was studied, including pre-processing steps such as image smoothing, image enhancement, threshold segmentation, and morphological processing, to extract feature parameters of weld defects. On this basis, a welding seam defect detection and classification system based on the cascade classifier and AdaBoost algorithm was designed, and efficient recognition and classification of welding seam defects were achieved by training the cascade classifier. The results show that the system can accurately identify and distinguish pits, holes, and undercutting defects in welds, with an average classification accuracy of over 90%. The detection and recognition rate of pit defects reaches 100%, and the detection accuracy of undercutting defects is 92.6%. And the overall missed detection rate is less than 3%, with both the missed detection rate and false detection rate for pit defects being 0%. The average detection time for each image is 0.24 s, meeting the real-time requirements of industrial automation. Compared with infrared and ultrasonic detection methods, the proposed machine-vision-based detection system has significant advantages in detection speed, surface defect recognition accuracy, and industrial adaptability. This provides an efficient and accurate solution for laser welding defect detection of automotive brake joints.

In order to improve the processing yield of large-size wafers, the partition control method of silicon carbide wafer heating plate heated by metal oxide conductive coating is studied, and a 4-partition temperature control system based on Smith fuzzy multilevel integral separation PID is proposed so as to solve the surface temperature consistency of the wafer heating plate under the requirement of high precision. Through the thermal effect simulation to establish the system model, the heat distribution in each region is analyzed. And then the heating plate temperature control system based on PLC controller is built, and Smith fuzzy multilevel integral separation PID algorithm to facilitate the practical application to improve the control accuracy is proposed, combined with the system model and the conventional PID algorithm in MATLAB for comparison. Finally the experimental verification is carried out based on the established heating plate temperature control system. Simulation and experimental results show that the present control method has a higher control accuracy, and can realize the consistency of the surface temperature of the heating plate stabilized at about 0.05℃, the average temperature steady-state error ±0.05℃, and the control effect is good. 为提高大尺寸晶圆的加工良率, 研究采用金属氧化物导电涂层加热的碳化硅晶圆加热盘分区控制方法, 提出基于Smith模糊多级积分分离PID的四分区温控系统, 解决高精度要求下晶圆加热盘表面温度一致性问题。通过热效应仿真建立系统模型, 分析各区域的热量分布, 进而基于PLC控制器搭建加热盘温度控制系统, 提出便于实际应用的Smith模糊多级积分分离PID算法提高控制精度, 在MATLAB中结合系统模型与常规PID算法进行对比, 最后基于所建立的加热盘温度控制系统进行实验验证。仿真结果与实验结果均表明, 所提控制方法具有更高的控制精度, 可以实现加热盘表面温度一致性稳定在0.05℃左右, 平均温度稳态误差±0.05℃, 控制效果良好。

In contrast to previous research that has primarily focused on distance synchronization of states in chaotic systems, shape synchronization emphasizes the geometric shape of the attractors of two chaotic systems. Diverging from the existing work on shape synchronization, this paper introduces the application of adaptive control methods to achieve asymptotic shape synchronization for the first time. By designing an adaptive controller using the proposed adaptive rule, the response system under control is able to attain asymptotic synchronization with the drive system. This method is capable of achieving synchronization for models with parameters requiring estimation in both the drive and response systems. The control approach remains effective even in the presence of uncertainties in model parameters. The paper presents relevant theorems and proofs, and simulation results demonstrate the effectiveness of adaptive asymptotic shape synchronization. Due to the pseudo-random nature of chaotic systems and their extreme sensitivity to initial conditions, which make them suitable for information encryption, a novel channel-integrated image encryption scheme is proposed. This scheme leverages the shape synchronization method to generate pseudo-random sequences, which are then used for shuffling, scrambling, and diffusion processes. Simulation experiments demonstrate that the proposed encryption algorithm achieves exceptional performance in terms of correlation metrics and entropy, with a competitive value of 7.9971. Robustness is further validated through key space analysis, yielding a value of 10210×2512, as well as visual tests, including center and edge cropping. The results confirm the effectiveness of adaptive asymptotic shape synchronization in the context of image encryption.

To improve the shortcomings of the traditional RRT (Rapid exploration of random tree) algorithm, the G-RRT is introduced as an enhanced robotic arm path planning method. The G-RRT can be also used to improve search efficiency and reduce computational complexity through the introduction of probability thresholds, dynamic step expansion and simplified random trees. The G-RRT is compared with conventional RRT and P-RRT (RRT with target bias strategy only) through simulation experiments. The results demonstrate the effectiveness of G-RRT and show optimized path planning. Furthermore, obstacle avoidance tests on conventional RRT and G-RRT algorithms show significant improvements in the latter. The G-RRT algorithm generates a 99% reduction in the number of random tree nodes, a 97.8% reduction in search time, and a 32% reduction in path length compared to the traditional RRT algorithm. The results show that the G-RRT algorithm takes less time to plan and the path is shorter. These improvements provide a reference for the application of intelligent technology in actual scenarios.

This study adopts the sol-gel method to prepare a TiO2 coating on the surface of the AZ91 magnesium alloy, hydrolyse C16H36O4Ti to generate the TiO2 coating and form a film with excellent corrosion resistance on the surface of an AZ91 magnesium alloy. The composition, surface structure and microstructure of the TiO2 coatings are characterised via X-ray diffraction (XRD) and scanning electron microscopy. The corrosion performance of the surface coatings was investigated through hydrogen evolution experiments and electrochemical tests. The results demonstrate that TiO2 sols prepared from a mixture of hydrochloric acid, deionised water, C16H36O4Ti and anhydrous ethanol can form stable layers on the surface of an AZ91 magnesium alloy after heat treatment. The results of hydrogen evolution experiments and electrochemical tests reveal that the TiO2 coating can effectively improve the corrosion resistance of the AZ91 magnesium alloy.

SiC particles were added to the Mg97Zn1Y2 alloy to improve its mechanical properties and damping properties. The microstructure, mechanical properties, and strain amplitude dependence of high-damping and high-strength SiC/Mg97Zn1Y2 magnesium matrix composites were analyzed. The strain amplitude-dependent damping of SiC/Mg97Zn1Y2 composites and the effect of SiC on this property were discussed herein. In anelastic damping, the strain amplitude-dependent damping curves of the composites were mainly divided into two sections, dominated by the G-L model. When the strain amplitude reaches a certain value, the dislocation motion inside the matrix becomes complicated. Moreover, the damping of the material could not be explained using the G-L model, and a new damping model related to microplastic deformation was proposed. In the anelastic damping stage, with the increase in the amount of the added SiC particles, the damping performance first increases and then decreases. Moreover, the damping value of the composite material is larger than that of the matrix alloy. In the microplastic deformation stage, the damping properties of the composites and matrix alloys considerably increase with the strain amplitude.

SiC particles were added to the Mg97ZnlY2 alloy to improve its mechanical properties and damping properties.The microstructure,mechanical properties,and strain amplitude dependence of high-damping and high-strength SiC/Mg97ZnlY2 magnesium matrix composites were analyzed.The strain amplitude-dependent damping of SiC/Mg97ZnlY2 composites and the effect of SiC on this property were discussed herein.In anelastic damping,the strain amplitude-dependent damping curves of the composites were mainly divided into two sections,dominated by the G-L model.When the strain amplitude reaches a certain value,the dislocation motion inside the matrix becomes complicated.Moreover,the damping of the material could not be explained using the G-L model,and a new damping model related to microplastic deformation was proposed.In the anelastic damping stage,with the increase in the amount of the added SiC particles,the damping performance first increases and then decreases.Moreover,the damping value of the composite material is larger than that of the matrix alloy.In the microplastic deformation stage,the damping properties of the composites and matrix alloys considerably increase with the strain amplitude.

In this study, MgO/Mg composites were prepared using direct melt oxidation to verify the effects of elastic deformation and microplastic deformation on the damping properties. It was found that the composites have high damping properties at a certain strain amplitude, which indicated that the damping properties of the magnesium matrix were effectively enhanced by the in situ-synthesized oxide particle. In addition, other damping mechanisms different from the G–L dislocation damping mechanism exist in MgO/Mg composites, i.e., the damping mechanism of the microplastic deformation, leading to a model of microplastic deformation damping established and its mechanistic analysis.