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Optimization of Joint Angle of Six-degree-of-freedom Robotic Arm Based on Multi-objective Genetic Algorithm
Abstract
In this paper, for the joint angle optimization problem of six-degree-of-freedom robotic arm, a path optimization method based on multi-objective genetic algorithm is proposed to enhance the robotic arm motion performance with the goal of minimizing the end error and energy consumption. Firstly, a forward kinematic model of the robotic arm is constructed based on the Denavit-Hartenberg (D-H) parameters, and the modeling and visualization in the zero-position state is realized through the MATLAB Robotics Toolbox, and the coordinates of the target point (1500mm, 1200mm, 200mm) are set. Then, the dual-objective optimization model including Euclidean distance of end position and motion energy consumption is established by combining the parameters of joint inertia (0.2-0.6 kg-m²) and average angular velocity (1.0-3.0 rad/s), and the non-dominated sorting genetic algorithm II (NSGA-II) is used to solve the Pareto optimal solution set. The optimization results show that the end error is as low as 0.0488 mm and the total energy consumption is reduced to 23.9282 J under the optimal configuration that takes into account both accuracy and energy consumption, which verifies the effectiveness of the method in robotic arm motion control. The research results provide theoretical reference and technical support for the optimization of robotic arm paths in industrial automation and precision operation.
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