Impedance control is an important method for robot manipulator to control the contact force. By expanding the traditional integer order impedance model to the fractional order model, a fractional order impedance (FO-impedance) controller is presented first in this paper. A FO-impedance controller parameters tunning strategy based on the frequency-domain is presented. A systematic FO-impedance control simulation system with robot manipulator, servo system, dynamic disturbance and dynamic feedforward is constructed. The influence of time-varying dynamic characteristics for robot FO-impedance system is analyzed. In order to achieve desired dynamic response and high robustness performance, a dynamic feedforward method is proposed and the effectiveness is verified in the simulation.
the lightweight, highly reliable assembly and testing technology of spacecraft cables is an important direction of spacecraft design, production, inspection research and development at home and abroad. Spacecraft cable network is an important hub for connecting spacecraft equipment, and is one of the key factors for the success or failure of spacecraft. With the development of materials, electronic technology and interconnection technology, there is still a lot of room for optimizing the weight of spacecraft cable network. This paper focuses on the lightweight method, typical defects and reliability evaluation technology of special cables for spacecraft.
Vision surveillance is an effective way to detect the water accumulation and corrosion in bridge cable anchorage zone. Duo to the closed dark environment and the high reflectivity of metal surfaces in the zone, the acquired images are partially too bright or too dark. It is adverse for vision surveillance. To solve this problem, we propose to capture multi-exposure images to improve the image quality of the anchorage zone by image fusion. Seven multi-exposure image fusion algorithms are tested in the experiments. Both subjective evaluation and objective evaluation show that all the fusion algorithms can effectively improve the image quality of the anchorage zone. Among the seven algorithms, MEF-OSSI is superior to the others, and it is recommended in the engineering application.
Due to the large number and variety of equipment to be tested in the chemical plant, and the limited computing power and storage resources of inspection robots in the chemical plant, the backbone network of the deep learning algorithm is huge, and the number of parameters and calculation is large. The deep learning detection algorithm runs slowly or cannot run on the robot embedded equipment, this paper makes a lightweight improvement on the deep learning detection algorithm. Firstly, the YOLOv5 network, which has higher detection accuracy and faster detection speed at present, is proposed to be used for the detection of leak-prone devices in chemical plants, and then the huge network structure of YOLOv5 is lightened and improved. The original CSP1_X backbone feature extraction network of the network is replaced by the ShuffleNetv2 network, the ordinary convolution in the original network is replaced by depth-separable convolution to reduce the size of the network model, and the neck network structure is trimmed to reduce the number of network parameters and computational effort. Using data augmentation to randomly flip, randomly crop, randomly cut, and scale the dataset data, which in turn expands the amount of data and improves the detection accuracy of the network. The improved YOLOv5-v2 network can work on embedded devices on inspection robots in chemical plants.
This paper proposes a multi-robot formation coverage method by combining the improved Artificial Potential Field (APF) with the Centroidal Voronoi Tessellation (CVT). Firstly, we added the distance factor to weaken the infinite repulsive force, so that the robots can reach the target near the obstacle. Secondly, we set the judgment condition of the local optimal, by exploring multiple directions of local optimal area, robots will get the suitable direction to leave. Finally, we propose the Centroidal Repulsion Field (CRF) to prevent the centroids generate inside the obstacle, which will improve the efficiency of obstacle avoidance during multi-robot coverage. The effectiveness of the proposed method is verified by simulation in MATLAB.
