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The purpose of this paper is to design a climbing robot connected by a connecting rod mechanism to achieve multi-functional tasks such as obstacles crossing and climbing of power transmission towers.

A connecting rod type gripper has been designed to achieve stable grasping of angle steel. Before grasping, use coordination between structures to achieve stable docking and grasping. By using the alternating movements of two claws and the middle climbing mechanism, the climbing and obstacle crossing of the angle steel were achieved.

Through a simple linkage mechanism, a climbing robot has been designed, greatly reducing the overall mass of the robot. It can also carry a load of 1 kg, and the climbing mechanism can perform stable climbing. The maximum step distance of the climbing robot is 543 mm, which can achieve the crossing of angle steel obstacles.

A transmission tower climbing mechanism was proposed by analyzing the working environment. Through the locking ability of the screw nut, stable clamping of the angle steel is achieved, and a pitch mechanism is designed to adjust the posture of the hand claw.

This paper aims to solve the problem of uncertain position and attitude between unstructured terrain robot and grasped target and insufficient control accuracy in extreme environment, a grasping mechanism based on attraction domain relationship is proposed, which can realize autonomous positioning, capturing and grasping of robot under low control accuracy.

The grasping mechanism was designed, taking inspiration from fishing behavior this mechanism introduces attraction domains and flexible-elastic structures through the active and passive ends to achieve automatic positioning and capture. After the capture is completed, the grasping mechanism connects the active end and the passive end, simultaneously relying on the gravity of the target object to achieve locking and release between the robot and the target object. This paper adopts theoretical, simulation and experimental verification methods to conduct theoretical and simulation analysis on the autonomous positioning and grasping process of the mechanism, and produces grasping experimental prototypes with different positions and postures.

The experiment shows that the gripping mechanism designed in this paper can achieve automatic positioning capture and gripping of large deviation situations under low control accuracy, with a displacement deviation of up to 10 mm (about 1/6 diameter of the end of the mechanism) and an angle deviation of up to 3°. The scientific research task in the extremely high altitude environment has finally been successfully accomplished.

Inspired by fishing behavior, this paper proposes a positioning, capturing and grasping mechanism. The attraction area built with permanent magnets, coupled with the flexible connection, enables precise capture under low control, while the grasping mechanism can also rely on gravity to self-lock and release.

Due to the miniaturization of electronic devices, the increased current density through solder joints leads to the occurrence of electromigration failure, thereby reducing the reliability of electronic devices. The purpose of this study is to propose a finite element-artificial neural network method for the prediction of temperature and current density of solder joints, and thus provide reference information for the reliability evaluation of solder joints.

The temperature distribution and current density distribution of the interconnect structure of electronic devices were investigated through finite element simulations. During the experimental process, the actual temperature of the solder joints was measured and was used to optimize the finite element model. A large amount of simulation data was obtained to analyze the neural network by varying the height of solder joints, the diameter of solder pads and the magnitude of current loads. The constructed neural network was trained, tested and optimized using this data.

Based on the finite element simulation results, the current is more concentrated in the corners of the solder joints, generating a significant amount of Joule heating, which leads to localized temperature rise. The constructed neural network is trained, tested and optimized using the simulation results. The ANN 1, used for predicting solder joint temperature, achieves a prediction accuracy of 96.9%, while the ANN 2, used for predicting solder joint current density, achieves a prediction accuracy of 93.4%.

The proposed method can effectively improve the estimation efficiency of temperature and current density in the packaging structure. This method prevails in the field of packaging, and other factors that affect the thermal, mechanical and electrical properties of the packaging structure can be introduced into the model.