Optimal Measurement Poses Using LSSA for Robot Kinematics-Flexibility Calibration

The absolute positioning of robots is a primary factor limiting their applications. In order to enhance the efficiency and precision of robot calibration, this study introduces a method that utilizes the Levy flight and Sparrow Search Algorithm (LSSA) to optimize the measurement pose of the robot, reducing the number of measurement poses and improving calibration accuracy. Firstly, an error model for robot positioning is established by considering the kinematic error model based on the product of exponentials (POE) and a flexible error model that accounts for the robot's own weight. Subsequently, an observability index for optimizing robot measurement poses is introduced, and LSSA is employed to optimize the most suitable subset of poses. Finally, the proposed robot measurement calibration optimization algorithm and calibration method are validated through simulations and experimental cases. The experimental results show that the proposed algorithm can effectively improve the absolute positioning accuracy of the robot, and the mean value of the end-effector position error is reduced from 0.898 mm to 0.291 mm while reducing the number of measurement poses.