In this paper, the kinematic accuracy problem caused by geometric errors of a 2(3HUS+S) parallel manipulator is described. The kinematic equation of the manipulator is obtained by establishing a D–H (Denavit–Hartenberg) coordinate system. A D–H transformation matrix is used as the error-modeling tool, and the kinematic error model of the manipulator integrating manufacturing and assembly errors is established based on the perturbation theory. The iterative Levenberg–Marquardt algorithm is used to identify the geometric errors in the error model. According to the experimentally measured attitudes, the kinematic calibration process is simulated using MATLAB software. The simulation and experiment results show that the attitude errors of the moving platforms after calibration are reduced compared with before the calibration, and the kinematic accuracy of the manipulator is significantly improved. The correctness and effectiveness of the error model and the kinematic calibration method of the 2(3HUS+S) parallel manipulator for simulation of hip joint motion are verified.

Based on product of exponentials (POE) formula, three explicit error models are given in this paper for kinematic calibration of serial robot through measuring its end-effector positions. To obtain these error models, the tool frame should be chosen as reference frame at first, and then each position–error-related segment in the error models using pose measurement should be selected. And during kinematic parameter identification, all the errors in joint twists are identifiable, and the initial transformation errors and the joint zero-position errors can be identified conditionally. Namely, the initial transformation errors are identifiable if they do not contain orientation errors. And the joint zero-position errors are identifiable when a robot only consists of prismatic joints and the coordinates of its joint twists are linearly independent.

The effectiveness of this calibration method has been validated by simulations and experiments. The results show that: (1) the identification algorithms are robust and practical. (2) The method of position measurement is superior to that of pose measurement.

In this paper, the optimization of architectural parameters for a class of translational parallel kinematic machine (PKM) is performed with the particle swarm optimization (PSO) to achieve the best accuracy characteristics. The conventional error transformation matrix (ETM) is derived based on the differentiation of kinematic equations, and a new error amplification index (EAI) over a usable workspace is proposed as an error performance index for the optimization. To validate the efficiency of the PSO method, both the traditional direct search method and the genetic algorithm (GA) are implemented as well. The simulation results not only show the advantages of PSO method for the architectural optimization, but also reveal the necessity to introduce the EAI for the optimal design. And the results are valuable for architectural design of the PKM for machine tool applications.