###### Abstract  
The spacecraft docking motion simulation system for on-orbit docking plays a very important role in some theoretical research and engineering application fields. The parallel robot utilized in the spacecraft docking simulation system requires high positioning and orientation accuracy to achieve better simulation results. A novel kinematic parameter identification method with an improved particle swarm optimization (PSO) algorithm is proposed to enhance positioning and orientation accuracy of the parallel robot. A fitness function is established using these residuals between the measured and computed poses by a coordinate measuring machine and forward kinematics. The kinematic parameter identification problem is turned into a high-dimensional nonlinear optimization in which the unknown kinematic parameter errors are regarded as optimal variables. The optimal variables are solved by the proposed improved PSO algorithm. The mean values of the positioning and orientation errors are reduced from 4.3268 mm and 0.2221 deg to 0.7692 mm and 0.0674 deg, respectively. The proposed kinematic parameter identification method increases the positioning accuracy mean by 22.26% and the orientation accuracy mean by 32.80% compared with the least squares method. The kinematic parameter identification method with the improved PSO algorithm can effectively enhance positioning and orientation accuracy of the parallel robot for docking motion simulation. parallel robot; parameter identification; particle swarm optimization algorithm; pose accuracy; nonlinear least squares +
Footnote †: journal: Applied  
The kinematic parameter error identification is the most important step of kinematic calibration. The kinematic parameter error identifications are usually expressed as a standard linear or nonlinear least squares optimization [19; 20; 21; 22]. The optimization problem can be solved by conventional or intelligent algorithms. [PERSON] et al. [23] presented a new optimal kinematic calibration method to obtain the geometric errors by utilizing the least error-sensitive areas in the workspace of parallel kinematic machines. [PERSON] et al. [24] established an error-mapping model and proposed parameter identification of a novel parallel manipulator with full-circle rotation with double ball-bar measurement data. [PERSON] et al. [25] analyzed the Newton iterative parameter identification method and developed a modified parameter identification method of a 3-DOF parallel tool head to simplify the algorithm parameter selection in the regularization identification method. [PERSON] et al. [26] constructed a dimensionless error model considering the geometrical characteristics of a Stewart platform and investigated the kinematic parameter error identification accuracy of the Stewart platform based on the least squares algorithm. [PERSON] et al. [27] investigated the identifiability of kinematic parameter errors of a 6-RUHU parallel manipulator and obtained a minimal identification model to realize kinematic calibration with a smaller number of random measurements. The kinematic parameter errors were identified by using the linear least squares algorithm. However, the least squares algorithm is liable to fall into local optimal solutions. A kinematic calibration of a portable coordinate measuring machine with parallel-joint based on the Gauss-Newton algorithm was put forward by [PERSON] et al. [28]. [PERSON] et al. [29] converted the kinematic calibration problem of serial robots into a separable nonlinear least squares problem. Then, the optimal kinematic parameters were identified by using the Gauss-Newton iterative algorithm. A comprehensive kinematic calibration procedure of hexapod machine tools was proposed by [PERSON] and [PERSON] [30], and the actual kinematic parameters were solved by minimizing a cost function based on the Levenberg-Marquardt algorithm. [PERSON] et al.