ABSTRACT As a critical link in the steel metallurgy process, the automation level of ladle masonry directly influences the service life of the ladle and metallurgical quality. Considering that the ladle masonry manipulator is susceptible to environmental disturbance, sensor noise, system parameter variations, and other disturbances in complex environments, this paper proposes an active disturbance rejection control method based on a fixed‐time observer. A multi‐power fixed‐time extended state observer is constructed to ensure that complex disturbances converge to the equilibrium point within a fixed time, enabling accurate estimation of total system disturbances within a fixed‐time framework. Simulation results demonstrate that this method effectively overcomes the degradation of observation performance in the extended state observer caused by large initial observation errors, thereby enhancing the trajectory tracking accuracy and robustness of the manipulator.

ABSTRACT This article addresses the trajectory tracking problem for a fully omnidirectional demolition robot (FM‐OMR) equipped with four Mecanum wheels under longitudinal wheel slippage and unknown disturbances. It proposes an event‐triggered model predictive control (EMPC) framework and introduces an adaptive exponential reach law sliding mode observer (AERL‐SMO) for online disturbance estimation. The AERL‐SMO suppresses the chattering inherent to classical sliding‐mode observers by using tanh‐based smoothing and a boundary‐layer saturation function, and it accelerates convergence for large errors through an adaptive exponential gain. The EMPC employs an event‐triggered strategy: the MPC is re‐solved only when the error energy exceeds a threshold, which significantly reduces the number of online QP solves. Simulation validation demonstrates that under identical constraints and disturbance conditions, the proposed method reduces MPC solution iterations from 1257 to 579 (a 53.85% decrease) while substantially lowering the mean absolute error (MAE) and disturbance estimation error. The article also presents stability analysis (based on Lyapunov) and parameter sensitivity discussions for the algorithm. This method is suitable for omnidirectional mobile robot platforms with limited computational resources that require resistance to wheel slippage and complex disturbances.

ABSTRACT Robotic manipulators have become essential in demolition tasks involving hazardous, confined, or structurally unstable environments, where precise and responsive motion control is critical for safety and efficiency. Solving the inverse kinematics (IK) of demolition robot manipulators poses considerable challenges due to the inherent strong nonlinearity and coupling in their kinematic equations, along with potential singularities that can undermine real‐time computational efficiency and system robustness. To address these issues, this study introduces an enhanced Newton–Raphson (NR) approach, specifically optimized for inverse kinematic analysis of 6‐DOF manipulators equipped with a spherical wrist architecture frequently adopted in demolition robotic arms. The proposed method strategically partitions the manipulator into two three‐DOF segments and constructs NR iterative equations by utilizing the reconnecting constraints between these kinematic substructures. After solving a subset of joint variables iteratively, the remaining two joint angles are obtained analytically. The experimental results show that: Compared with the traditional Newton–Raphson method, the improved method has a faster convergence speed, higher accuracy, and better robustness, especially when dealing with singular points at the wrist. This makes the improved method applicable to the inverse kinematics control of dismantling robotic arms in complex environments.

ABSTRACT The NSGA‐II algorithm is widely applied in multiobjective mechanical structure optimization. In this study, the NSGA‐II algorithm was adopted to optimize the refractory lining structure of a ladle. First, a parametric model of the ladle was established using ANSYS Workbench, and the temperature and stress fields under typical operating conditions were calculated to generate the sample data required for training a BPNN prediction model. Second, to address the limitations of conventional BPNN, a genetic algorithm was employed to optimize the initial weights and thresholds. Taking the thicknesses of the working layer, permanent layer, and insulation layer as design variables, a GA‐BPNN single‐objective prediction model was developed, enabling high‐precision predictions of ladle mass, ladle volume, maximum ladle shell temperature, and maximum refractory lining stress. Finally, the NSGA‐II algorithm was utilized to solve the multiobjective optimization problem of the ladle refractory lining. In this optimization, ladle mass and capacity were imposed as constraints, while the maximum shell temperature and maximum lining stress were defined as objectives. In the simulation experiments, thermo‐mechanical coupling analysis was performed in ANSYS Workbench to generate 81 training samples and 10 test samples of temperature and stress data. The GA‐BPNN model optimized by the genetic algorithm achieved accurate predictions of ladle mass, volume, shell temperature rise, and lining stress. The results demonstrated that when the insulation, permanent, and working layers were 9.992, 83.998, and 137 mm thick, respectively, the ladle mass, volume, maximum shell temperature, and maximum lining stress reached 57,972.525 kg, 14.298 m 3 , 145.549°C, and 43.621 MPa. Under this parameter combination, the ladle exhibited optimal comprehensive performance in terms of insulation and service life. This method provides an effective approach to determining the optimal refractory lining structure of ladles, with significant implications for improving thermal performance, extending service life, and enhancing industrial economic efficiency.

ABSTRACT In recent years, intelligent control methods based on surface electromyographic signals (sEMG) have received extensive attention in the fields of bionic prosthetics and human‐computer interaction. Compared with high‐density sampling, sparse sampling sEMG has significant advantages due to its convenient collection, strong non‐intrusiveness and high equipment flexibility. However, it has deficiencies in spatial resolution and muscle activity information acquisition, which can easily lead to a decline in the recognition accuracy of complex gestures. In view of the limitations of sparse sEMG in feature extraction and channel information utilization, this paper proposes a hybrid neural network SERes‐L based on the attention mechanism. This model introduces the squeeze‐and‐excitation (SE) attention module in the residual unit to construct a residual attention structure to enhance the channel modeling of key information. At the same time, it combines the long short‐term memory network (LSTM) to capture the long‐term temporal dependencies in gesture actions, achieving a deep integration of spatial and temporal features. Experiments on the self‐built dataset show that SERes‐L achieves an average recognition accuracy rate of 93.90%. To verify its generalization ability, further tests were conducted on three public datasets, NinaPro DB1, DB4, and DB5. The average classification accuracy rates of the model reached 90.28%, 84.72%, and 91.87% respectively, significantly outperforming many existing mainstream methods. The above results indicate that the proposed SERes‐L architecture can effectively alleviate the challenges of insufficient utilization of channel information and inadequate feature extraction in sparse sEMG, demonstrating excellent generalization performance and practical application potential.

ABSTRACT The ladle is a critical piece of equipment for transporting high‐temperature molten steel in the steelmaking process, and its operational performance directly impacts the quality of the final product, energy efficiency, and overall production costs. With the advancement of continuous casting and external refining technologies, the stability of ladles under high‐temperature and high‐intensity service conditions faces increasingly stringent demands, particularly as the issue of thermal stress damage to the refractory lining becomes more pronounced. Based on typical steelmaking conditions, this study establishes a multi‐stage service cycle model for a 350‐ton ladle. Utilizing a parameterized finite element approach, we conduct a coupled transient thermo‐mechanical simulation to analyze the temperature and stress fields, specifically investigating the synergistic effects of thermal expansion, temperature gradient, and ferrostatic pressure. The results demonstrate that thermal stress is the predominant factor responsible for lining damage. Notably, the thermal expansion behavior of the refractory materials exerts a significant influence on the distribution and magnitude of thermal stress within the ladle structure. In contrast, mechanical loads such as the static pressure from the molten steel contribute minimally to the overall stress state. Furthermore, the study reveals that the stress on the ladle shell significantly reduces after the refractory lining fails and expansion pressure diminishes, quantitatively highlighting the critical role of interfacial expansion constraints. This research provides a comprehensive theoretical foundation and valuable engineering insights for the optimized design and longevity enhancement of ladle lining structures.

Autonomous driving has gradually become a research hotspot in recent years, but the robustness of loopback detection in complex environments such as dynamic and weak textures need to be improved. For the problem that traditional visual SLAM doesn’t make sufficient use of semantic information in the environment, a dynamic feature rejection method for visual SLAM is proposed based on geometric constraints in autonomous driving. This paper proposes to combine image segmentation and visual SLAM to build a semantic SLAM system and also proposes a data association method that combines semantic and geometric information to improve the traditional loopback detection method by using semantic information to increase the accuracy of loopback detection. The result of experiment on the TUM public dataset shows that the loopback detection accuracy of the improved loopback detection method is higher than that of the bag-of-words method in all four datasets; therefore, the method in this paper improves the accuracy of loopback detection of the SLAM system in general.

sEMG(surface electromyography) signals have been widely used in rehabilitation medicine in the past decades because of their non-invasive, convenient and informative features, especially in human action recognition, which has developed rapidly. However, the research on sparse EMG in multi-view fusion has made less progress compared to high-density EMG signals, and for the problem of how to enrich sparse EMG feature information, a method that can effectively reduce the information loss of feature signals in the channel dimension is needed. In this article, a novel IMSE (Inception-MaxPooling-Squeeze- Excitation) network module is proposed to reduce the loss of feature information during deep learning. Then, multiple feature encoders are constructed to enrich the information of sparse sEMG feature maps based on the multi-core parallel processing method in multi-view fusion networks, while SwT (Swin Transformer) is used as the classification backbone network. By comparing the feature fusion effects of different decision layers of the multi-view fusion network, it is experimentally obtained that the fusion of decision layers can better improve the classification performance of the network. In NinaPro DB1, the proposed network achieves 93.96% average accuracy in gesture action classification with the feature maps obtained in 300ms time window, and the maximum variation range of action recognition rate of individuals is less than 11.2%. The results show that the proposed framework of multi-view learning plays a good role in reducing individuality differences and augmenting channel feature information, which provides a certain reference for non-dense biosignal pattern recognition.

Autonomous driving has gradually become a research hotspot in recent years, but the robustness of loopback detection in complex environments such as dynamic and weak textures needs to be improved. A semantic loopback detection method is proposed based on instance segmentation and visual SLAM to make sufficient use of semantic information in autonomous driving. The proposed method combines image segmentation and visual SLAM (Simultaneous Localization and Mapping) to construct a semantic SLAM system. What’s more, a data association method that combines semantic and geometric information is proposed to improve the traditional loopback detection method by using semantic information to increase the accuracy of loopback detection. The result of experiment on the TUM public dataset shows that the loopback detection accuracy of the improved loopback detection method is higher than that of the bag-of-words method in all four datasets, and our proposed algorithm can effectively improve the accuracy of loopback detection of the SLAM system in general.

Autonomous driving has gradually become a research hotspot in recent years. Visual Simultaneous Localization and Mapping (SLAM) technology can help unmanned vehicles accurately explore the environment at a lower cost, and the readability of the map can be improved by integrating target detection algorithms. However, the location and 3D shape of the object in the map were not obtained. The method of RGBD-SLAM based on object detection with two-stream YOLOv4-MobileNetv3 convolutional neural network is proposed in this paper. RGBD SLAM algorithm and target detection algorithm are combined to build an algorithm model that can generate the global sparse map and build target dense map quickly. The two-stream network is integrated to obtain 2D information about the target, and further combined with the camera pose after the front-end key frame detection of the SLAM algorithm in this paper, and the dense 3D point cloud of the target and the center point position of the object is obtained. Then, the sparse point cloud of the SLAM system and the dense point cloud of the target can be obtained. The experimental results show that the number of point clouds decreases by about 50% and the time for mapping accounts for about 60% of the global dense mapping time. The method of this paper can efficiently decrease the computational space and improve the speed of semantic mapping, which verifies its feasibility and superiority. It can be used to achieve large-area mapping and the ability to update maps during autonomous driving.

Summary With the wide application of stereovision in SLAM, object pose estimation has gradually become one of the research hotspots. This article proposes an object pose estimation for robotic grasping based on stereo vision with improved K‐D tree ICP algorithm. The feature points and feature descriptors of the point cloud of the object to be captured are extracted, and the feature template set is established. The SAC‐IA algorithm is used to carry out initial registration of the point cloud of the target, and the ICP algorithm based on K‐D tree is used for fine registration. The experimental results show that the average coincidence degree of the final registration of the proposed object pose estimation method reaches 94.1%, and the accurate 6D pose of the object to be grasped is obtained.

Summary Target localization in unknown environment is one of the development directions of mobile robots. Simultaneous localization and mapping (SLAM) can be used to build maps in unknown environments, but it has the problem of poor readability and interactivity. In this article, target detection and SLAM are combined to search and locate the target by using rich RGBD images information. The determined position in the global map is conducive to the follow‐up operation of the target by mobile robots. By establishing a local dense point cloud map of the target object, the current state of the target object is directly displayed, the readability of the map is improved, and the disadvantages of difficult understanding of the global sparse map and slow construction of the global dense map are avoided. A target localization algorithm under the framework of yolov4 is designed to apply in the process of SLAM global mapping. Our works are helpful for obtaining positions of objects in three‐dimensional space. The experimental results show that the time‐consuming of this method in dense mapping is reduced by 50%–70%, and the number of point clouds is also reduced by 60%–70%.

Summary For the problem of surface electromyography (sEMG) gesture recognition, considering the fact that the traditional machine learning model is susceptible to the sEMG feature extraction method, it is difficult to distinguish the subtle differences between similar gestures. The NinaPro DB1 dataset is used as the research object, and the sEMG feature image and the Convolutional Neural Network (CNN) are combined to recognize 52 gesture movements. The CNN model effectively solves the limitations of traditional machine learning in sEMG gesture recognition, and combines 1‐dim convolution kernel to extract deep abstract features to improve the recognition effect. Finally, the simulation experiment shows that compared with the accuracy of the raw‐sEMG images based on the CNN and the sEMG‐feature‐images based on the CNN and sEMG based on the traditional machine learning, the multi‐sEMG‐features image based on the CNN is the highest, which coming up to 82.54%.

Summary With the continuous development of sensor technology, the acquisition cost of RGB‐D images is getting lower and lower, and gesture recognition based on depth images and Red‐Green‐Blue (RGB) images has gradually become a research direction in the field of pattern recognition. However, most of the current processing methods for RGB‐D gesture images are relatively simple, ignoring the relationship and influence between its two modes, and unable to make full use of the correlation factors between different modes. In view of the above problems, this paper optimizes the effect of RGB‐D information processing by considering the independent features and related features of multi‐modal data to construct a weight adaptive algorithm to fuse different features. Simulation experiments show that the method proposed in this paper is better than the traditional RGB‐D gesture image processing method and the gesture recognition rate is higher. Comparing the current more advanced gesture recognition methods, the method proposed in this paper also achieves higher recognition accuracy, which verifies the feasibility and robustness of this method.

Summary Gesture recognition has always been a research hotspot in the field of human‐computer interaction. Its purpose is to realize the natural interaction with the machine by recognizing the semantics expressed by gesture. In the process of gesture recognition, the occlusion of gesture is an inevitable problem. In the process of gesture recognition, some or even all of the gesture features will be lost due to the occlusion of the gesture, resulting in the wrong recognition or even unrecognizability of the gesture. Therefore, it is of great significance to study gesture recognition under occlusion. The single shot multibox detector (SSD) algorithm is analyzed, and the front‐end network is compared. Mobilenets is selected as the front‐end network, and the Mobilenets‐SSD network is improved. In tensorflow environment, based on the improved network model, the self‐occlusion gesture and object occluding gesture are trained in color map, depth map, and color and depth fusion respectively. The recognition models of self‐occlusion gestures and object‐occlusion gestures in color map, depth map, and color and depth fusion are obtained. And compare and analyze the learning rate, loss function, and average accuracy of various models obtained for occlusion gesture recognition.

Abstract Robot grasping technology is a hot spot in robotics research. In relatively fixed industrialized scenarios, using robots to perform grabbing tasks is efficient and lasts a long time. However, in an unstructured environment, the items are diverse, the placement posture is random, and multiple objects are stacked and occluded each other, which makes it difficult for the robot to recognize the target when it is grasped and the grasp method is complicated. Therefore, we propose an accurate, real‐time robot grasp detection method based on convolutional neural networks. A cascaded two‐stage convolutional neural network model with course to fine position and attitude was established. The R‐FCN model was used as the extraction of the candidate frame of the picking position for screening and rough angle estimation, and aiming at the insufficient accuracy of the previous methods in pose detection, an Angle‐Net model is proposed to finely estimate the picking angle. Tests on the Cornell dataset and online robot experiment results show that the method can quickly calculate the optimal gripping point and posture for irregular objects with arbitrary poses and different shapes. The accuracy and real‐time performance of the detection have been improved compared to previous methods.

Summary For meeting the comprehensive requirements of “super insulation,” “lightweight,” and “longevity” of the contemporary ladle, this article designs a new kind of lightweight ladle with heat preservation and longevity performance, and based on steady‐state analysis method and numerical simulation technology, the comparison of temperature distribution between new lightweight and traditional ladle under typical operating modes is made and analyzed. The simulation results of temperature field prove that the performance of heat preservation in new kind of lightweight ladle has been improved obviously from the two aspects of ladle shell temperature and molten steel file rate. At the same time, the simulation results of stress field indicate that the stress of designed lightweight ladle reduced and distributed more evenly, which is conducive to prolonging the work time in‐service of the ladle. Finally, based on the field test, the simulation is proved to be effective, and the designed ladle structure achieves the expected purpose.