###### Abstract  
In this study, a manipulator calibration algorithm is suggested to decrease the positional errors of an industrial robotic manipulator using a genetic algorithm to select optimal measurement poses. First, a genetic algorithm based on the observability index is used for the selection of optimal measurement poses. By employing the selected optimal poses, conventional kinematic calibration is used to identify the geometric errors of the robot. Finally, to further improve the positional accuracy of the robot, compliance errors are compensated by a radial basis function neural network based on effective torques. The proposed method provides a novel and effective way to select optimal measurement poses for the calibration process using a genetic algorithm and enhances the accuracy of the robot manipulators by constructing a relationship between the effective torque and the compliance errors using a radial basis function. The results of the experimental calibration and validation processes carried out on a YS100 robot show the effectiveness of the proposed method in comparison with the other calibration approaches. genetic algorithm; optimal measurement poses; robot accuracy; radial basis function; robot calibration 202212542212542212542  
kinematic parameters. However, the approach assumed that the joint compliance parameters were constant. In [9,20,21], calibration methods were proposed that considered both geometric and non-geometric errors. [PERSON] et al. [22] suggested a procedure for approximating compliance errors where torque sensors were used through a polynomial equation of the joint and link parameters. These calibration methods have been successfully applied to industrial robotic manipulators, but the methods simply and randomly select the poses of the robot for the calibration process. To perform robot calibration, the measurement is an important step. The collected measurement data are served as constraint equations on the parameters to be identified. However, the data acquisition for experimental robot calibration always contains noise. These inevitable errors degrade the calibration accuracy. To enhance the stability of the calibration algorithm with respect to measurement errors, [PERSON] and [PERSON] [23] increased the number of constraint equations. However, this approach took a lot of time and effort. Another approach is to improve the numerical quality of the measurements. To find an optimal set for robot configurations, numerous approaches have been used [24, 25, 26]. Recently, [PERSON] et al. suggested a calibration method for a serial robot that used a multiple identification space [24]. An optimization model with two observability indexes was introduced by [PERSON] et al. [25]. A universal observability index was used in the work of [PERSON] et al. [26]. These methods depend on the relevant observability index of the measurement configurations. A calibrated set that has a maximum observability index generates a better robustness of the calibration process. Nevertheless, the above methods only dealt with conventional kinematic calibration. This work introduces a novel calibration method for improving the precision of robotic manipulators. An optimal robot configuration set was first selected by a genetic algorithm (GA) based on the observability index. GAs created by [PERSON] and his colleagues [27] have been applied to numerous optimization problems. Due to the advantages of GAs [28]--such as parallel searching, stability for noise, and easy evasion from the local minima--configuration sets for optimal measurement poses were identified. Kinematic calibration was then used to determine the D-H constraints of the manipulator. Finally, using effective torques, a radial basis function (RBF) compensator was employed to compensate for the compliance errors. Our method not only proposed the use of a GA for selecting the optimal poses to calibrate the robot, but also combined the optimal pose method and kinematic calibration method (KM).