Boundary Encryption-Based Monte Carlo Learning Method for Workspace Modeling

Abstract

Abstract As an important branch of machine learning, Monte Carlo learning has been successfully applied to engineering design optimization and product predictive analysis, such as design optimization of heavy machinery. However, the accuracy of the classical Monte Carlo algorithm is not high enough, and the existing improved Monte Carlo algorithm has a complex calculation process and difficult parameter control. In this paper, the Monte Carlo method based on boundary point densification is proposed to calculate workspace. This paper takes the calculation of 2000T offshore crane workspace as an example to verify the effectiveness and practicability of the algorithm. The D-H method is used to establish the workspace model of the offshore crane. The calculation method of crane workspace based on the Monte Carlo learning method with increased boundary point density is discussed in detail, and the correctness of crane workspace is verified. The steps of the algorithm include generate the basic space, extract and draw the boundary, increase the density of boundary points, and cyclic. The rationality of the method is proved by comparing the simulation results with the design experience and calculated values.