In recent years, wall-climbing robots have begun to replace manual work at heights to reduce economic losses and casualties caused by working at heights. This paper designs a negative pressure adsorption type wall-climbing robot and analyzes the internal fluid movement state of its negative pressure device and the force analysis of the robot when it is adsorbed and balanced. Furthermore, through the experimental prototype, the influence of wall material, robot pose, negative pressure cavity shape and sealing method on the adsorption performance of the wall-climbing robot is explored. The computational fluid dynamics simulation (CFD) simulation method and experimental results are used to verify each other, which proves the correctness of the simulation results. Based on the Kriging surrogate model, the functional relationship between the impeller blade outlet angle, the impeller inlet diameter, the number of blades as the design variables, the negative pressure as the dependent variable was established, and the genetic algorithm (GA) was used to optimize it. Compared with the original design, the optimized design results of impeller parameters have increased the negative pressure value from 3534.75 to 4491.19 Pa, an increase of 27.06%.

With the development and upgrading of intelligent mines, research on the unmanned walking of intelligent electric shovels (ES) has been carried out to improve the moving efficiency of extra-large excavators. This paper first introduces an electric shovel’s primary moving condition in an open-pit mine. According to the moving characteristics of the heavy-duty crawler, the artificial potential field (APF) algorithm is improved to plan the moving trajectory of the electric shovel and carry out simulation verification. A dynamic model of an electric shovel is established. A fuzzy control tracking method is proposed based on preview displacement and centroid displacement deviation. The robustness of the tracking algorithm is verified by multi-condition simulation. Finally, the electric shovel prototype is tested through path planning and tracking experiments. The experimental results show that the improved artificial potential field algorithm can plan an obstacle-free path that satisfies the movement of an electric shovel, and the electric shovel can quickly track the preset trajectory. The maximum deviation of the track tracking center of mass is no more than 10 cm, and the deviation of the heading angle when the shovel reaches the endpoint is within 2°.

This article summarizes the research significance and the development status of the unmanned ground tracked vehicles (UGTVs). According to the speed and steering principle of the UGTVs in plane motion, the kinematic state space equation of the vehicle is established. Based on the model predictive control (MPC), the UGTVs trajectory tracking controller is also established. After introducing the overall control system solution, based on the vehicle model, the track speed on both sides is used as the control input to solve the predicted output of the system. After constraining the control amount, the model prediction controller is established by using S function. Several simulations with different preset speeds are performed under linear conditions, and the numerical simulation with different prediction time domains and control time domains are performed under continuous curves. The test validates the effectiveness of the controller, and the effects of speed and time domain parameters on the deviation are analyzed based on the results. Based on satellite positioning technology, trajectory tracking tests of the UGTVs are carried out. After analyzing the positioning principle and common errors, the real-time kinematic technology is used to improve the positioning accuracy of the vehicle prototype. The hardware of the test platform includes a data acquisition system, a control execution system and a walking execution system, and the software part includes data processing and optimization control. The trajectory tracking experiments under different driving conditions are conducted, and the results show that the navigation tracking system can achieve good tracking performance.

In this paper, a crawler-type wall-climbing robot with negative pressure adsorption is designed. The dynamic model of the robot is established based on the method of discretizing the force load of the track shoe. The correctness of the kinematic model and dynamic model is proved by comparing two different ways of virtual prototype simulation and prototype experiment. According to the simulation results of the dynamic model and virtual prototype, the influence of design parameters on the motion performance of the wall-climbing robot is analyzed. The smaller the speed difference between the two tracks of the robot, the longer the gauge between the two tracks, and the greater the inclination of the adsorption wall, the larger the turning radius of the robot will be. The research content based on this paper can provide a new design optimization method and theoretical basis for improving crawler wall-climbing robots and crawler vehicles.