The paper proposes a new M2ACD(Multi-Actor-Critic Deep Deterministic Policy Gradient) algorithm to apply trajectory planning of the robotic manipulator in complex environments. First, the paper presents a general inverse kinematics algorithm that transforms the inverse kinematics problem into a general Newton-MP iterative method. The M2ACD algorithm based on multiple actors and critics is structured. The dual-actor network reduces the overestimation of action values, minimizes the correlation between the actor and value networks, and mitigates instability during the actor’s selection process caused by excessively high Q-values. The dual-critic network reduces the estimation bias of Q-values, ensuring more reliable action selection and enhancing the stability of Q-value estimation. Secondly, The robotic manipulator’s TSR (two-stage reward) strategy is designed and divided into the approach and close. Rewards in the approach phase focuses on safely and efficiently approaching the target, and rewards in the close phase involves final adjustments before contact is made with the target. Thirdly, to solve the position hopping jitter problem in traditional reinforcement learning trajectory planning, the NURBS(Non-Uniform Rational B-Splines) curve is used to smooth the hopping trajectory generated by M2ACD. Finally, the correctness of the M2ACD and the kinematics algorithm is verified by experiments. The M2ACD algorithm demonstrated superior curve smoothing, convergence stability and convergence speed compared to the TD3, DARC and DDPG algorithms. The M2ACD algorithm can be effectively applied to collaborative robots’ trajectory planning, establishing a foundation for subsequent research.

This study introduces a parallel YOLO–GG deep learning network for collaborative robot target recognition and grasping to enhance the efficiency and precision of visual classification and grasping for collaborative robots. First, the paper outlines the target classification and detection task, the grasping system of the robotic arm, and the dataset preprocessing method. The real-time recognition and grasping network can identify a diverse spectrum of unidentified objects and determine the target type and appropriate capture box. Secondly, we propose a parallel YOLO–GG deep vision network based on YOLO and GG-CNN. Thirdly, the YOLOv3 network, pre-trained with the COCO dataset, identifies the object category and position, while the GG-CNN network, trained using the Cornell Grasping dataset, predicts the grasping pose and scale. This study presents the processes for generating a target’s grasping frame and recognition type using GG-CNN and YOLO networks, respectively. This completes the investigation of parallel networks for target recognition and grasping in collaborative robots. Finally, the experimental results are evaluated on the self-constructed NEU-COCO dataset for target recognition and positional grasping. The speed of detection has improved by 14.1%, with an accuracy of 94%. This accuracy is 4.0% greater than that of YOLOv3. Experimental proof was obtained through a robot grasping actual objects.

Pneumonia is a respiratory infection that affects the lungs. The symptoms of viral and bacterial pneumonia are similar. In order to improve automatic detection efficiency regarding X-ray images of pneumonia, this paper, we propose a novel pneumonia detection method based on the Fast-YOLO network model. First, we re-annotated the open-source dataset of MIMIC Chest X-ray pneumonia, enhancing the model’s adaptability to complex scenes by incorporating Mixup, Mosaic, and Copy–Paste augmentation methods. Additionally, CutMix and Random Erasing were introduced to increase data diversity. Next, we developed a lightweight FASPA Fast Pyramid Attention Mechanism and designed the Fast-YOLO network based on this mechanism to effectively address the complex features in pneumonia X-ray images, such as low contrast and an uneven distribution of local lesions. The Fast-YOLO network improves upon the YOLOv11 architecture by replacing the C3k2 module with the FASPA attention mechanism, significantly reducing the network’s parameter count while maintaining detection performance. Furthermore, the Fast-YOLO network enhances feature extraction capabilities when handling scenes with geometric deformations, multi-scale features, and dynamic changes. It expands the receptive field, thereby balancing computational efficiency and accuracy. Finally, the experimental results demonstrate that the Fast-YOLO network, compared to traditional convolutional neural network methods, can effectively identify pneumonia regions and localize lesions in pneumonia X-ray image detection tasks, achieving significant improvements in FPS, precision, recall, mAP @0.5, and mAP @0.5:0.95. This confirms that Fast-YOLO strikes a balance between computational efficiency and accuracy. The network’s excellent generalization capability across different datasets has been validated, showing the potential to accelerate the pneumonia diagnostic process for clinicians and enhance diagnostic accuracy.

This article proposes a CBAM-ASPP-SqueezeNet model based on the attention mechanism and atrous spatial pyramid pooling (CBAM-ASPP) to solve the problem of robot multi-target grasping detection. Firstly, the paper establishes and expends a multi-target grasping dataset, as well as introduces and uses transfer learning to conduct network pre-training on the single-target dataset and slightly modify the model parameters using the multi-target dataset. Secondly, the SqueezeNet model is optimized and improved using the attention mechanism and atrous spatial pyramid pooling module. The paper introduces the attention mechanism network to weight the transmitted feature map in the channel and spatial dimensions. It uses a variety of parallel operations of atrous convolution with different atrous rates to increase the size of the receptive field and preserve features from different ranges. Finally, the CBAM-ASPP-SqueezeNet algorithm is verified using the self-constructed, multi-target capture dataset. When the paper introduces transfer learning, the various indicators converge after training 20 epochs. In the physical grabbing experiment conducted by Kinova and SIASUN Arm, a network grabbing success rate of 93% was achieved.

This paper studies the AWC (Active Wafer Centering) algorithm for the movement control and wafer calibration of the handling robot in semiconductor manufacturing to prevent wafer surface contact and contamination during the transfer process. The mechanical and software architecture of the wafer-handling robot is analyzed first, which is followed by a description of the experimental platform for semiconductor manufacturing methods. Secondly, the article utilizes the geometric method to analyze the kinematics of the semiconductor robot, and it decouples the motion control of the robot body from the polar coordinates and joint space. The wafer center position is calibrated using the generalized least-square inverse method for AWC correction. The AWC algorithm is divided into calibration, deviation correction, and retraction detection. These are determined by analyzing the robot’s wafer calibration process. In conclusion, the semiconductor robot’s motion control and AWC algorithm are verified through experiments for correctness, feasibility, and effectiveness. After the wafer correction, the precision of AWC is <± 0.15 mm, which meets the requirements for transferring robot wafers.

This paper analyzes traditional vibration suppression methods in order to solve the vibration problem caused by the stiffness of flexible industrial robots. The principle of closed-loop control dynamic feedforward vibration suppression is described as the main method for solving robot vibration suppression. This paper proposes a method for time-lag filtering based on T-trajectory interpolation, which combines the T-planning curve and the time-lag filtering method. The method’s basic principle is to dynamically adjust the trajectory output through the algorithm, which effectively suppresses the amplitude of the harmonic components of a specific frequency band to improve the vibration response of industrial robot systems. This experiment compared traditional vibration suppression methods with the time-lag filtering method based on T-trajectory interpolation. A straight-line method was proposed to measure the degree of vibration. The results demonstrate that the time-lag filtering method based on T-trajectory interpolation is highly effective in reducing the vibration of industrial robots. This makes it an excellent option for scenarios that demand real-time response and high-precision control, ultimately enhancing the efficiency and stability of robots in performing their tasks.