Robot path planning is an important component of ensuring the robots complete work tasks effectively. Nowadays, most maps used for robot path planning obtain relevant coordinate information through sensor measurement, establish a map model based on coordinate information, and then carry out path planning for the robot, which is time-consuming and labor-intensive. To solve this problem, a method of robot path planning based on ant colony algorithms after the standardized design of non-standard map grids such as photos was studied. This method combines the robot grid map modeling with image processing, bringing in calibration objects. By converting non-standard actual environment maps into standard grid maps, this method was made suitable for robot motion path planning on non-standard maps of different types and sizes. After obtaining the planned path and pose, the robot motion path planning map under the non-standard map was obtained by combining the planned path and pose with the non-standard real environment map. The experimental results showed that this method has a high adaptability to robot non-standard map motion planning, can realize robot path planning under non-standard real environment maps, and can make the obtained robot motion path display more intuitive and convenient.

This study focuses on predicting mechanical fatigue in excavator turntables, critical components susceptible to failure due to variable operational loads. While conventional methods like finite element analysis(FEA) and multiaxial fatigue criteria have been used, they are limited by the complexity and cost of obtaining real operational load spectra. To address this challenge, our research presents a comprehensive approach that integrates multi-body dynamics modeling, finite element analysis, and MATLAB-based fatigue life prediction systems. Our methodology involves creating a finite element model for stress analysis, synthesizing load spectra from operational data, and utilizing Weibull distribution to analyze load magnitude probabilities. Subsequently, MATLAB imported the load spectrum and built the fatigue prediction framework to finalize the analysis. Furthermore, we have fully open-sourced our code on an open platform, incorporating default load profiles and predictive models within the code. Key findings pinpoint areas prone to stress concentration and fatigue. Key findings identify stress concentration areas and fatigue-prone regions, providing valuable insights for design optimization and durability improvement.

The map used for the path planning of mobile robots is mostly a grid map, which is generated through independent design or sensor measurement to obtain relevant information and then modeling. To obtain the robot motion planning path more quickly, this paper proposes a method of robot motion path planning through the ant colony algorithm under a non-standard environment map. The non-standard environment map is used for standard grid design, and the grid map is optimized by the method of no safety distance obstacle added box selected and safety distance obstacle added box selected, then the path planning is carried out through the ant colony algorithm. In addition, the mutual correspondence between grid maps and real environment maps was solved by adding calibration objects. The experimental results show that this method can not only effectively solve the problem of ant colony algorithms under a non-standard real environment map, show the planning path and pose on the non-standard real environment map, moreover, the safety degree of the planning path in the real environment is also increased by 29.51%, ensuring the safety degree of the whole planning path, which improves the intelligent degree of robot motion path planning.

Wear debris image processing techniques are increasingly employed in health monitoring and fault diagnosis for mechanical equipment. However, during the acquisition and storage of wear debris images, substantial noise is often introduced, leading to significant errors in the subsequent feature extraction and health status assessment. Moreover, the denoising process frequently encounters algorithmic shortcomings, resulting in blurred boundary information, noise artifacts, and incomplete boundaries, greatly hindering further research on wear debris images. Thus, this paper proposes the wavelet-K-singular value decomposition-edge (W-KSVD-EDGE) algorithm, which initially performs two-dimensional discrete wavelet decomposition on noisy images through a multi-band decomposition module, dividing them into four sub-bands of high and low frequencies to enhance image denoising performance. Simultaneously, a second-order decomposition is performed, followed by reconstruction, which reduces high-frequency noise and lowers the complexity of subsequent image processing operations. Subsequently, the K-singular value decomposition (KSVD) algorithm is applied to denoise each sub-band of the initially reconstructed images. Applying the KSVD algorithm to images of different frequency bands, its complexity is mitigated, and the efficiency of image denoising is increased. Consequently, the boundaries of the reconstructed images are further optimized using an improved Canny algorithm for edge enhancement, incorporating edge detection coefficients, resulting in better boundary information restoration for wear debris images. Finally, by analyzing the wear debris information of oil samples collected by a ferrograph, the W-KSVD-EDGE algorithm is employed for denoising both ordinary and wear debris images. The results are evaluated using both subjective and objective methods. The effectiveness and versatility of the proposed algorithm are thereby demonstrated.

The lightweight design of box-type double-girder overhead crane main girders is important for improving load-carrying capacity, reducing energy consumption, and enhancing transportation efficiency. However, the structural optimization of crane main girders involves multiple constraints and strong nonlinearity, which often leads to slow convergence and premature stagnation when using traditional optimization methods. To address these issues, a multi-strategy improved dung beetle optimization algorithm (MSIDBO) is proposed for the lightweight design of overhead crane main girders. First, the search mechanism and inherent limitations of the standard dung beetle optimization (DBO) algorithm are analyzed. Subsequently, several enhancement strategies are introduced, including hybrid chaotic population initialization; reflective boundary handling; adaptive quantum jump updating; adaptive hybrid updating; and a staged control strategy for search intensity. These strategies are designed to enhance population diversity and achieve a better balance between global exploration and local exploitation. The performance of MSIDBO was evaluated on 29 CEC2017 benchmark functions. The results show that MSIDBO generally converges faster on 25 functions and reaches the global optimum on 24 functions among the compared algorithms. Finally, based on mechanical analysis and design specifications of overhead crane main girders, a constrained structural optimization model is established. The lightweight design optimization is carried out, and finite element simulations were conducted using ANSYS Workbench to verify the effectiveness and engineering feasibility of the optimized design. The results show that the proposed MSIDBO algorithm exhibits enhanced stability and convergence performance, achieving a weight reduction of 19.4% in the main girder under the specified design configuration, meeting satisfying strength and safety requirements.