This article proposes a handling-stability control strategy for distributed drive electric vehicles (EVs) to improve motion performance. A motion supervisor, using only front steering angle feedback, is developed to evaluate the driving state and generate a unified yaw rate reference for handling-stability coordination. To ensure tracking convergence, a Lyapunov-based nonlinear model predictive control (LNMPC) strategy is proposed for direct yaw moment control (DYC), incorporating a contraction constraint to guarantee closed-loop stability, with rigorous proofs provided. For rapid problem-solving, a modified iterative linear quadratic regulator (iLQR) algorithm is developed, leveraging a relaxed log barrier function and double-loop iteration to handle inequality constraints, preventing violations and theoretically ensuring convergence to the original problem’s solution. Additionally, an auxiliary control law is applied to generate the initial solution in iLQR, reducing sensitivity. Using a Karush-Kuhn-Tucker (KKT) conditions-based approach, the virtual control distribution is optimized efficiently, and the torque command of in-wheel motors (IWMs) can be gained. Simulations and hardware-in-the-loop (HIL) experiments demonstrate superior handling-stability performance and high computational efficiency with the proposed strategy.

Underwater object detection is critical for marine ecological monitoring and biodiversity research, yet existing algorithms struggle in detecting densely packed objects of varying sizes, particularly in occluded and complex underwater environments. This study introduces Underwater-Yolo, a novel detection network that enhances performance in these challenging scenarios by integrating a dual-branch occlusion-handling attention mechanism (GLOAM) and a Cross-Stage Partial Dilated Deformable Convolution (CSP-DDC) backbone. The dilated deformable convolutions (DDCs) in the backbone and neck expand the receptive field, thereby improving the detection of small objects, while the deformable convolutions enhance the model’s adaptive feature extraction capabilities for unstructured objects. Additionally, the CARAFE up-sampling operator in the neck aggregates contextual information across a broader spatial domain. The GLOAM, consisting of a global branch (using a Vision Transformer to capture global features and object–background relationships) and a local branch (enhancing the detection of occluded objects through depthwise–pointwise convolutions), further optimizes performance. By incorporating these innovations, the model effectively addresses the challenges of detecting small and occluded objects in dense underwater environments. The evaluation on the CLfish-V1 dataset shows significant improvements over state-of-the-art algorithms, with an AP50 of 93.8%, an AP75 of 88.9%, and an AP-small of 76.4%, marking gains of 4.7%, 16.7%, and 6%, respectively. These results demonstrate the model’s effectiveness in complex underwater scenarios.

Due to the little variation in defect points, tile block defect detection typically detects subtle defects in large-format images, allowing defective characteristics to be displayed regionally. Traditional convolutional neural network architectures that extract regional features take into account the connection between regional features simply, resulting in the presence of region-specific bias, which makes tile block defect detection still a challenging task. To address this challenge, this paper divides feature information into patches that can represent different regional features. Additionally, the relationship between different patches and tile block defects is studied; as a result, this paper proposes a new attention mechanism called the Cross Patch Attention Module (CPAM). Since the regional performance of patches is consistent with the tile block defect characteristics, CPAM can distinguish various regional features by patches. Then, in order to create reliable one-dimensional patch information, CPAM provides a method to connect patches linearly in two spatial directions. This takes into account the correlation of adjacent patches in various spatial directions. Finally, by extracting the regional characteristics of patches, CPAM can successfully assist the model in distinguishing the importance of different patches. The experimental results demonstrate that CPAM has excellent performance for tile block defect detection, and plugging CPAM into different end-to-end models can have a good gain effect, which can effectively and stably help the model to complete the task of tile block defect detection.

When performing robotic automatic sorting and assembly operations of multi-category hardware, there are some problems with the existing convolutional neural network visual recognition algorithms, such as large computing power consumption, low recognition efficiency, and a high rate of missed detection and false detection. A novel efficient convolutional neural algorithm for multi-category aliasing hardware recognition is proposed in this paper. On the basis of SSD, the novel algorithm uses Resnet-50 instead of VGG16 as the backbone feature extraction network, and it integrates ECA-Net and Improved Spatial Attention Block (ISAB): two attention mechanisms to improve the ability of learning and extract target features. Then, we pass the weighted features to extra feature layers to build an improved SSD algorithm. At last, in order to compare the performance difference between the novel algorithm and the existing algorithms, three kinds of hardware with different sizes are chosen to constitute an aliasing scene that can simulate an industrial site, and some comparative experiments have been completed finally. The experimental results show that the novel algorithm has an mAP of 98.20% and FPS of 78, which are better than Faster R-CNN, YOLOv4, YOLOXs, EfficientDet-D1, and original SSD in terms of comprehensive performance. The novel algorithm proposed in this paper can improve the efficiency of robotic sorting and assembly of multi-category hardware.

To improve the adaptive clamping performance of traditional single-tendon-driven underactuated grippers for grasping multiple categories of objects, a novel dual-tendon-driven underactuated gripper is proposed in this paper. First, two independent tendons with different winding paths are designed in the gripper to realize the changeable resultant moment of the end knuckle rotating joint and the movement sequences of gripper knuckles driven by different tendons are analysed too. Then, some kinematic analysis and dynamical simulations are carried out to verify the validation of the knuckle structure and dual-tendon winding path design. At last, a prototype of the novel gripper is manufactured and some grasping experiments are carried out on multiple categories of objects, with different sizes and shapes. The experimental results show that all the objects can be clamped tightly. Compared with the traditional single-tendon-driven gripper, the novel one can achieve a more flexible grasping operation and a larger end clamping force, which are more suitable for the adaptive grasping requirements of robotic automatic sorting.