In the article “ABC”, Bai Yonghua proposed a new calibration method for robot kinematics parameters to improve the positioning accuracy of robots. The author put forward an improved batfish foraging optimization algorithm to identify errors in robot kinematics parameters, and presented a low-cost measurement method using a 3D scanner to measure the end position of the robot.
The author believes that the positioning accuracy of robots has a great impact on their applications. To improve the positioning accuracy without changing the structure of robots, the author modified the robot kinematics model. The sources of robot errors were summarized, and existing calibration methods and their limitations were analyzed. Existing measurement methods are expensive and require extensive professional knowledge. Parameter identification algorithms have slow convergence speed and low accuracy.
To ensure the completeness, continuity, and minimality of the error model for robot position, the author analyzed its redundancy and removed redundant parameters from it.
To address shortcomings in existing identification algorithms, the author proposed an improved batfish foraging optimization algorithm to identify errors in kinematic parameters. The mathematical model and improvement methods of this algorithm were detailedly introduced by the author. Natural selection strategy was used to increase convergence speed while adaptive parameter control strategy coordinated global search and local search.
