In order to solve the problem that the trajectory is not straight when piecewise polynomial interpolation method is used to plan the trajectory of excavator's linear operation in joint space, which leads to the inaccurate trajectory, this paper proposes a quintic polynomial interpolation method based on multidimensional trajectory to realize the accurate trajectory of excavator's linear operation. While using Matlab to draw the relevant images in motion, ikunc function is used to optimize the inverse solution of each point in the motion process, and joint Angle trajectory smoothing algorithm and particle swarm optimization algorithm are used to verify the minimum change of joint Angle in the optimal inverse solution of ikunc function. It is verified that the ikunc function is correct to obtain the minimum and best inverse solution of the joint Angle change, which indirectly achieves the energy optimal effect.

A monitoring system for transportation of material ropeway of transmission line is designed for the difficulty in fully understanding the construction process and component stress state of material ropeway of transmission line. Based on wireless ad hoc network technology, tension sensors, high-precision positioning modules, and ad hoc network devices are integrated to achieve real-time collection and transmission of monitoring data for material ropeway transportation, such as tension of carrying rope, load of steering pulley, load of running car, position of running car, speed of material transportation. By combining the refined calculation method of material ropeway, the tension of the working rope during the transportation process of the material ropeway is calculated, which is compared with the monitoring data, and the visual display of the monitoring data and image data of the material ropeway transportation is achieved to realize the real-time monitoring of the material ropeway transportation status during the construction process. Through verification tests in transmission line engineering, the effectiveness and accuracy of the monitoring system for transportation of material ropeway of transmission line have been verified, which provides technical support for the transportation safety during the construction of the material ropeway.

To solve automated guided vehicle (AGV) dispatching problem in situations such as mutual interference between the operating equipment and transportation network complexity makes loading and unloading system more complicated. The changes of quay and yard crane operation sequence and operational efficiency, and the uncertainty of AGV operating time. Thus a shared online AGV dispatching strategy combining shared dispatching and online dispatching is introduced to solve AGV dispatching problem. A mathematical model is proposed to describe AGV dispatching problem. AGA is introduced to solve this problem. Experiment with different AGV amounts shows the validity of the proposed method. With a strong reference meaning to AGV dispatching decision making.

In order to meet the operation requirements of the Tianjin trunk line water conveyance box culvert of the South to North Water Transfer Project, a special multifunctional operation vehicle is developed and its high precision control is realized. Firstly, the structure and working mechanism of the multifunctional operation vehicle are described, and its kinematics models are established respectively. Secondly, a cross coupling synchronization control strategy is proposed to solve the problem of vehicle body attitude deviation caused by speed non-synchronization in dual motor independent control. Finally, the system model of multifunctional vehicle is built and the simulation analysis of cross coupling synchronization control is completed. The results of software simulation and system engineering application show that the proposed cross coupling synchronous control strategy can effectively improve the non-synchronization errors of double motors and improve the attitude control accuracy of the multifunctional vehicle for box culverts, and has strong engineering practicability and application popularization.

Aiming at the problems of multi-dimensional small failure probability and implicit function in the reliability analysis for bridge crane structure, that is difficult to solve using traditional reliability analysis methods, the one based on dynamic Kriging surrogate model combined with important sampling method is proposed. Firstly, the dynamic Kriging surrogate model of the implicit function is constructed utilizing two learning functions, and then the most probable failure point (MPP) is obtained by using the improved first order second moment method combined with the Kriging surrogate model. Secondly, the importance sampling density function is constructed with the MPP as the central point. Finally, the reliability degree is calculated by the importance sampling method based on the established surrogate model. By introducing a new stop criterion combined with learning function, the number of finite element calls is reduced. Verified by an engineering case, the proposed method can well balance the model accuracy, result error and calculation cost.

In order to investigate the effect of tower slewing motion on tip displacement and to provide a basis for collecting tip displacement data to improve the accuracy of tower steel structure damage diagnosis, this paper adopts a tower physical structure experiment bench to select three different strokes of high, medium and low slewing speeds, and to collect tip displacement during slewing motion and static tip displacement. A characterization study of the tip displacement for slewing stroke and speed change was carried out; the structural response characteristics of the slewing motion were established and characterized by noise characterization coefficients. The experimental results show that the collected tip displacements are close to static displacements when the slewing stroke is larger, the slewing speed is slower, and the noise characterization coefficient is smaller than a set threshold. The purpose of this study is to avoid the influence of slewing motion on the real-time monitoring of structural damage, and to give a scientific basis for the setting of the data acquisition conditions for the required tower steel structure damage diagnosis.

Lightweight design is an effective way to improve the economic and environmental protection of static pile driver, as the key component of the static pile driver, the combined fixture is important for its lightweight design. To realize the combined fixture structure safety and lightweight design goal, a three-dimensional model is established by using solid works, and ANSYS Workbench is used to analyze and evaluate the equivalent stress of the whole structure and main components under the condition of pile pressing. Based on the results of analysis and evaluation, the topology optimization method of superposition replacement is adopted to carry out the structural optimization design, while meeting the needs of safety and engineering practice, its total quality is reduced by about 30%, and the economic and environmental benefits are obvious.

To achieve rapid prediction of aerodynamic noise in the rearview mirror area of vehicles, an stochastic noise generation and propagation (SNGR) method is adopted. Unlike general aerodynamic noise simulation methods, this method is based on the reynolds-averaged navier-stokes (RANS) equation to solve the steady flow field, reconstructing the sound source term through a velocity random model, and finally using the finite interpolation method to solve the acoustic analogy equation, greatly reducing the computational period of aerodynamic noise simulation. Based on actual vehicle data, a wind tunnel model is established, with a speed of 120 km/h as the simulation condition. Under the same conditions, the SNGR method and the general unsteady method using large eddy simulation as the flow field calculation model are respectively used for simulation. The results show that the calculation time of the SNGR method is greatly reduced, and the calculation results are consistent in the frequency range of 500-5 000 Hz, proving the efficiency of this method. On this basis, the flow field results in the rearview mirror area are analyzed, and the design of the car rearview mirror is optimized based on the principle of aerodynamic noise generation. Install the rearview mirrors before and after optimization on the entire vehicle for wind tunnel experiments, and verify the accuracy of the SNGR method calculation results by comparing the sound pressure level reduction before and after rearview mirror optimization. The simulation and experimental error of this method is between 4%-5%, proving that this method can be used in the optimization stage of aerodynamic noise performance of vehicle rearview mirrors.

The paper introduces mechanical character of boom for luffing jib crane, structural form and construction type of the inverted-Y boom are explained. A conversion section is arranged in the middle of the boom, the hoisting or luffing wire rope enters the boom head after bypassing the conversion section, the load of the boom is reduced by the pull of the wire rope. The example of derrick crane with maximum rated lifting capacity of 500 t is given, calculation of the boom shaft pressure, boom dead weight bending moment, wire rope tension and other loads on the boom chord of the magnitude of the axial force exerted, and analyze its proportion. It shows that the effect of hoisting or luffing wire rope can effectively reduce the dead weight bending moment of the boom, and offset part of the boom chord and belly rod axial force, improve the overall stability and bearing capacity of the boom, thus the design of the boom is optimized.

This paper proposes a robotic grasping technique based on object recognition and fully convolutional grasp quality convolutional neural network (FC-GQCNN). To address the limitations of traditional GQCNN, such as low computational efficiency and redundant feature calculations, an improved FC-GQCNN is developed. By replacing the fully connected layers in GQCNN with 1×1 convolutional layers, the proposed network can handle input images of arbitrary sizes. Furthermore, the integration of FC-GQCNN with the YOLOv8 object detection algorithm forms a YOLOv8-FCGQCNN cascade structure, effectively solving the challenges of object recognition and localization in complex environments. Experimental results demonstrate that this method achieves an 86% grasp success rate across 10 different objects, with an average detection time of 0.09 s per frame, which is 22 times faster than traditional GQCNN, significantly improving system efficiency. This method can accurately detect the grasping position of the object of interest and has higher reliability than the baseline method.