Research on path and energy consumption control of six-axis manipulator based on multi-objective optimization method

ACKNOWLEDGEMENTS: This work was supported by national college student innovation and entrepreneurship training program project of Changzhou Institute of Technology(Grant No.: 202411055026Z) and ministry of education industry-university cooperative education project (Grant No.: 231106441092432)

Abstract

In the present rapidly evolving industrial and technological domains, robotic arms have emerged as crucial automated equipment for enhancing production efficiency, accuracy, and safety. The six-degree-of-freedom robotic arm has been extensively utilized in multiple fields like industrial production, precise operation, operations in hazardous environments, and logistics on account of its capacity to undertake complex tasks. Nevertheless, in complex circumstances, the path optimization and joint angle optimization of robots have consistently been significant factors influencing their working efficiency. This paper intends to optimize the path and joint angles of the six-axis robotic arm through establishing a multi-objective optimization model, with the aim of improving end accuracy and reducing energy consumption.

For work one, a mathematical model for the robotic arm was crafted using the Denavit-Hartenberg (D-H) parameter method, enabling the construction of a homogeneous transformation matrix and the establishment of a forward kinematics model. This foundation allowed for the formulation of a nonlinear optimization model, which, coupled with the particle swarm optimization (PSO) algorithm, was used to minimize the end effector's positional error, achieving a significant reduction to 1.0×10^-3 mm and thus enhancing the arm's precision. For work two, expanding upon work one, an energy consumption model was developed to account for both kinetic and potential energies, ensuring that the terminal error remained within an acceptable range. A multi-objective optimization model was formulated with the goal of minimizing both terminal error and energy consumption. The PSO algorithm was employed once more, yielding an optimal joint angle configuration that resulted in minimal energy consumption of 23.9261 J and a terminal error of 3.044×10^-8 mm. For work Three, In addressing obstacle navigation, a raster map was created, and the A* algorithm was utilized to plan the optimal movement path for the robotic arm's base. This approach, built upon the previously established model, determined an optimal joint angle configuration that effectively circumvents obstacles while maintaining high operational accuracy and energy efficiency, with a terminal error of 4.028×10^-7 mm. For work four, the work of grasping multiple target points amidst obstacles was modeled as a Traveling Salesman work (TSP). A genetic algorithm was applied to this model to determine the optimal path for sequential visits to target points and calculate the joint angles, terminal error values, and energy consumption for each point. This resulted in a maximum terminal error within an acceptable range and an optimized energy consumption of 111.9223 J.

This study significantly advances the operational efficiency and accuracy of robotic arms in complex environments through robust mathematical modeling and optimization algorithms. It contributes to the evolution of robotic arm technology towards greater intelligence, flexibility, and efficiency, setting a foundation for broader applications in future industrial and technological advancements.