Research on Optimization of Joint Angle Path for a 6-DoF Robotic Arm Based on Kinematics and Genetic Algorithm

In industrial applications and automation, robotic manipulators play a crucial role. Several complex robotic systems perform numerous industrial tasks, including painting, welding, assembly, pick-and-place operations, and more. The position of the end-effector and the joint angles are of utmost importance, and their optimization directly impacts the manipulator's motion accuracy and work efficiency. Therefore, this paper proposes an optimization model for the joint angle path of a 6-degree-of-freedom (6-DoF) robotic arm based on forward and inverse kinematics and the Genetic Algorithm (GA). Firstly, using the Denavit-Hartenberg (D-H) parameters and the zero-position state, a simplified diagram of the 6-DoF robotic arm is drawn using the Robotics Toolbox in MATLAB. Then, a single-objective nonlinear optimization model is established with the goal of minimizing both the end-effector error and the total rotated joint angles. Initially, the joint angles and the posture of the robotic arm after grasping are obtained through forward and inverse kinematics analysis. Subsequently, by assessing whether the joint angles of the robotic arm after grasping satisfy the constraint conditions, the Analytic Hierarchy Process (AHP) is employed to determine the weights of minimizing the end-effector error and the total rotated joint angles in the objective function. The GA is then utilized to solve the model. Finally, this paper discusses and analyzes the established model, providing a new perspective for the optimization of the joint angle path of a robotic arm.