The mechanical structure and control system for a new crawler type of robots is presented in this paper. The type of the robot is designed for power transmission line inspections. The new crawler type of the robot is composed of three arms and a balance mechanism, which are all joined together through a base. The distributed and hierarchical control strategy for this new robot is proposed in combination with a rule based expert system for optimal decision making. The proposed control strategy has been organized into three levels hierarchically. Compared with other robot structures, this new type of robot has a simpler structure. It has been proved that this type of new robot has higher efficiency in obstacle crossing through the simulation and experiments operating with the prototype of the model machine. Its intelligent control strategy has demonstrated its competitive advantages. The crawler type inspection robot is a attempt for developing a new robot structure to be used for high voltage power transmission line inspection.

In order to accurately identify the high voltage transmission line fittings and guide the line patrol robot to achieve the corresponding obstacle-crossing action according to different fittings, a real-time detection network based on deep learning DeblurGANv2 and YOLOv5 target detection algorithm was proposed. In view of the problems of large network model, large amount of computation and low operation efficiency of YOLOv5 algorithm, the lightweight improvement of YOLOv5 algorithm was carried out to improve the detection speed of the algorithm. Shufflenetv2 was used to replace CSPDarknet-53 in the original network, and ECA module was introduced into Shufflenetv2.Enhance the model’s focus on valid features. At the same time, DeblurGANv2 super-resolution reconstruction algorithm is introduced to deblur the image of the fittings to generate high-quality images and improve the recognition accuracy. Experimental results based on homemade datasets show that,compared with the original YOLOv5 model, the parameter amount of the proposed method is reduced by 50.2%, the weight file size is only 5.3M, a reduction of 62.7%, and mAP@ (0.5) is increased by 1.3%. The proposed method can successfully detect the high voltage transmission line fittings and ensure the accuracy of fuzzy target detection.

As an important part of a micro-compressed air energy storage system, the scroll expander directly affects the efficiency of the whole energy storage system. The effects of resistance on the efficiency of scroll expander caused by inlet structure and size are discussed with theory analysis and experimental methods in this paper. Micro-compressed air energy storage system has aroused widespread attention because of its pollution-free, high flexibility, in the community, remote areas power supply. Comprehensive experimental work with the selections of different size and structure of the air inlet of the scroll expander was performed with the cutting angle of air inlet chamber of the scroll expander. The results of the experiments are discussed on how exergy efficiency and inlet flow of the scroll expander were affected resulting from the cutting angles dissected. The results show that a maximum value exists for exergy efficiency of the scroll expander. Therefore, the exergy efficiency of the scroll expander can be effectively improved by enlarging the air inlet port dimension and modifying the size of air chamber.