To address the problems that most convolutional neural network-based image defogging algorithm models capture incomplete global feature information and incomplete defogging, this paper proposes an end-to-end convolutional neural network and vision transformer hybrid image defogging algorithm. First, the shallow features of the haze image were extracted by a preprocessing module. Then, a symmetric network structure including a convolutional neural network (CNN) branch and a vision transformer branch was used to capture the local features and global features of the haze image, respectively. The mixed features were fused using convolutional layers to cover the global representation while retaining the local features. Finally, the features obtained by the encoder and decoder were fused to obtain richer feature information. The experimental results show that the proposed defogging algorithm achieved better defogging results in both the uniform and non-uniform haze datasets, solves the problems of dark and distorted colors after image defogging, and the recovered images are more natural for detail processing.

Surface defect detection of strip steel is an important guarantee for improving the production quality of strip steel. However, due to the diverse types, scales, and texture structures of surface defects on strip steel, as well as the irregular distribution of defects, it is difficult to achieve rapid and accurate detection of strip steel surface defects with existing methods. This article proposes a real-time and high-precision surface defect detection algorithm for strip steel based on YOLOv7. Firstly, Partial Conv is used to replace the conventional convolution blocks of the backbone network to reduce the size of the network model and improve the speed of detection; Secondly, The CA attention mechanism module is added to the ELAN module to enhance the ability of the network to extract detect features and improve the effectiveness of detect detection in complex environments; Finally, The SPD convolution module is introduced at the output end to improve the detection performance of small targets with surface defects on steel. The experimental results on the NEU-DET dataset indicate that the mean average accuracy (mAP@IoU = 0.5) is 80.4%, which is 4.0% higher than the baseline network. The number of parameters is reduced by 8.9%, and the computational load is reduced by 21.9% (GFLOPs). The detection speed reaches 90.9 FPS, which can well meet the requirements of real-time detection.

This paper introduces a pneumatic finger cylinder servo control system for medical grabbing. First, according to the physical structure of the proportional directional valve and the pneumatic cylinder, the state equation of the gas in the servo system was obtained. The Stribeck friction compensation model of a pneumatic finger cylinder controlled by a proportional valve was established and the experimental platform built. To allow the system output to better track the change in the input signal, the flow-gain compensation method was adopted. On this basis, a friction compensation control strategy based on a differential evolution algorithm was proposed and applied to the position control system of a pneumatic finger cylinder. Finally, the strategy was compared with the traditional proportional derivative (PD) strategy and that with friction compensation. The experimental results showed that the position accuracy of the finger cylinder position control system can be improved by using the friction compensation strategy based on the differential evolution algorithm to optimize the PD parameters.

Pneumatic actuators and electric actuators have almost been applied to all manufacturing industries. The two kinds of actuators can replace each other in most fields, such as the point to point transmission occasion and some rotating occasions. However, there are very few research results about the advantages and disadvantages of two kinds of actuators under the same working conditions so far. In this paper, a novel comprehensive assessment method, named as overall life cycle comprehensive assessment (OLCCA), is proposed for comparison and assessment of pneumatic and electric actuators. OLCCA contains mechanical properties evaluation (MPE), life cycle cost analysis based on users (LCCABOU) and life cycle environmental impact analysis (LCEIA) algorithm in order to solve three difficult problems: mechanical properties assessment, cost analysis and environmental impact assessment about actuators. The mechanical properties evaluation of actuators is a multi-objective optimization problem. The fuzzy data quantification and information entropy methods are combined to establish MPE algorithm of actuators. Two kinds of pneumatic actuators and electric actuators with similar bearing capacity and similar work stroke were taken for example to verify the correctness of MPE algorithm. The case study of MPE algorithm for actuators verified its correctness. LCCABOU for actuators is also set up. Considering cost complex structure of pneumatic actuators, public device cost even method (PDCEM) is firstly presented to solve cost division of public devices such as compressors, aftercooler, receivers, etc. LCCABOU method is also effective and verified by the three groups of pneumatic actuators and electric actuators. Finally, LCEIA model of actuators is established for the environmental impact assessment of actuators. LCEIA data collection method and model establishment procedure for actuators are also put forward. With Simapro 7, LCEIA comparison results of six actuators can be obtained: Fossil fuels are the major environmental factor of pneumatic and electric actuators; Environmental impact of electric actuator is large than one of pneumatic actuator under the similar mechanical properties and working conditions of pneumatic and electric actuators. The results are correct and correspond with the actual mechanical properties of actuators. This paper proposes a comprehensive evaluation method of the actuators, which can solve the critical problem that similar electromechanical products are very difficult to be compared with each other from the angle of performance, cost and environment impact.

Continuous robots have attracted more and more attention from the robotics community due to their high degree of flexibility and pliability, and have shown great potential for application in a variety of fields. With the continuous progress of material science, control technology, and artificial intelligence, the performance and application range of soft robotics have been further expanded, in which the cable drive has the advantages of large workspace, high flexibility, etc. The cable-driven soft robotic arm serves as an ultra-redundant robot that can operate in cramped and confined environments. In this paper, a cable-driven soft robot based on soft continuums and a cross gimbal is presented. The kinematics of the cable-driven soft robot is modeled and the mapping relations of the kinematics are solved by the D–H method and piecewise constant curvature, and the relations between the cable length, joint angle, and pose are further derived. Finally, the motion space of the cable-driven soft robot in the three-dimensional coordinate system is obtained by MATLAB2021b, and the single-segment soft body is simulated and analyzed using ADAMS to compare the theoretical data with the actual data and verify the reliability of this structure and method.

The friction interference in the pneumatic rotary actuator is the primary factor affecting the position accuracy of a pneumatic rotary actuator servo system. The paper proposes an evolutionary algorithm-based friction-forward compensation control architecture for improving position accuracy. Firstly, the basic equations of the valve-controlled actuator are derived and linearized in the middle position, and the transfer function of the system is further obtained. Then, the evolutionary algorithm-based friction feedforward compensation control architecture is structured, including that the evolutionary algorithm is used to optimize the controller coefficients and identify the friction parameters. Finally, the contrast experiments of four control strategies (the traditional PD control, the PD control with friction feedforward compensation without evolutionary algorithm tuning, the PD control with friction feedforward compensation based on the differential evolution algorithm, and the PD control with friction feedforward compensation based on the genetic algorithm) are carried out on the experimental platform. The experimental results reveal that the evolutionary algorithm-based friction feedforward compensation greatly improves the position tracking accuracy and positioning accuracy, and that the differential evolution-based case achieves better accuracy. Also, the system with the friction feedforward compensation still maintains high accuracy and strong stability in the case of load.