Path optimization model of manipulator based on d-h parameter method and genetic algorithm

作者：Peiqi He, Yaqin Zhao, Min Tan, Bo Liao · 发表于：Journal of Physics Conference Series · 年份：2025 · DOI：10.1088/1742-6596/2964/1/012018 · 被引用次数：4 · 研究领域：Robotic Path Planning Algorithms、Wireless Sensor Networks and IoT、Simulation and Modeling Applications

Abstract The Denavit-Hartenberg (D-H) parametric method is extensively utilized in the industrial machinery domain. This research explores the application of the D-H parametric method to enhance the functionality of six-degree-of-freedom manipulators, leveraging their notable flexibility and versatility to improve productivity and execution accuracy in highly automated industrial and service environments. In this research, the forward kinematics model of the manipulator is developed using the D-H parametric method. Subsequently, an optimization model is formulated with the objective of minimizing end-effector error and optimized through a genetic algorithm. Following single-objective optimization, the study incorporates energy consumption minimization as an additional objective. A dual-objective optimization model, which considers both end-effector error and energy consumption, is then constructed and optimized using a genetic algorithm. The results demonstrate a reduction in energy consumption of the six-degree-of-freedom manipulator and optimization of joint angle paths while ensuring that the end-effector error remains within an acceptable range. This research offers valuable insights for achieving efficient and precise operations in complex industrial applications and contributes to the advancement of automation technology.