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High precision assembly processes using industrial robots require the process parameters to be tuned to achieve desired performance such as cycle time and first time through rate. Some researchers proposed methods such as design-of-experiments (DOE) to obtain optimal parameters. However, these methods only discuss how to find the optimal parameters if the part and/or workpiece location errors are in a certain range. In real assembly processes, the part and/or workpiece location errors could be different from batch to batch. Therefore, the existing methods have some limitations. This paper aims to improve the process parameter optimization method for complex robotic assembly process.

In this paper, the parameter optimization process based on DOE with different part and/or workpiece location errors is investigated. An online parameter optimization method is also proposed.

Experimental results demonstrate that the optimal parameters for different initial conditions are different and larger initial part and/or workpiece location errors will cause longer cycle time. Therefore, to improve the assembly process performance, the initial part and/or workpiece location errors should be compensated first, and the optimal parameters in production should be changed once the initial tool position is compensated. Experimental results show that the proposed method is very promising in reducing the cycle time in assembly processes.

The proposed method is practical without any limitation.

The proposed technique is implemented and tested using a real industrial application, a valve body assembly process. Hence, the developed method can be directly implemented in production.

This paper provides a technique to improve the assembly efficiency by compensating the initial part location errors. An online parameter optimization method is also proposed to automatically perform the parameter optimization process without human intervention. Compared with the results using other methods, the proposed technology can greatly reduce the assembly cycle time.

This chapter compares the performance of the maximum simulated likelihood (MSL) approach with the composite marginal likelihood (CML) approach in multivariate ordered-response situations. The ability of the two approaches to recover model parameters in simulated data sets is examined, as is the efficiency of estimated parameters and computational cost. Overall, the simulation results demonstrate the ability of the CML approach to recover the parameters very well in a 5–6 dimensional ordered-response choice model context. In addition, the CML recovers parameters as well as the MSL estimation approach in the simulation contexts used in this study, while also doing so at a substantially reduced computational cost. Further, any reduction in the efficiency of the CML approach relative to the MSL approach is in the range of nonexistent to small. When taken together with its conceptual and implementation simplicity, the CML approach appears to be a promising approach for the estimation of not only the multivariate ordered-response model considered here, but also for other analytically intractable econometric models.

Both geometric and non-geometric parameters have noticeable influence on the absolute positional accuracy of 6-dof articulated industrial robot. This paper aims to enhance it and improve the applicability in the field of flexible assembling processing and parts fabrication by developing a more practical parameter identification model.

The model is developed by considering both geometric parameters and joint stiffness; geometric parameters contain 27 parameters and the parallelism problem between axes 2 and 3 is involved by introducing a new parameter. The joint stiffness, as the non-geometric parameter considered in this paper, is considered by regarding the industrial robot as a rigid linkage and flexible joint model and adds six parameters. The model is formulated as the form of error via linearization.

The performance of the proposed model is validated by an experiment which is developed on KUKA KR500-3 robot. An experiment is implemented by measuring 20 positions in the work space of this robot, obtaining least-square solution of measured positions by the software MATLAB and comparing the result with the solution without considering joint stiffness. It illustrates that the identification model considering both joint stiffness and geometric parameters can modify the theoretical position of robots more accurately, where the error is within 0.5 mm in this case, and the volatility is also reduced.

A new parameter identification model is proposed and verified. According to the experimental result, the absolute positional accuracy can be remarkably enhanced and the stability of the results can be improved, which provide more accurate parameter identification for calibration and further application.

With the improvement of modern aircraft requirements for safety, long life and economy, higher quality aircraft assembly is needed. However, due to the manufacturing and assembly errors of the posture adjustment mechanism (PAM) used in the digital assembly of aircraft large component (ALC), the posture alignment accuracy of ALC is difficult to be guaranteed, and the posture adjustment stress is easy to be generated. Aiming at these problems, this paper aims to propose a calibration method of redundant actuated parallel mechanism (RAPM) for posture adjustment.

First, the kinematics model of the PAM is established, and the influence of the coupling relationship between the axes of the numerical control locators (NCL) is analyzed. Second, the calibration method based on force closed-loop feedback is used to calibrate each branch chain (BC) of the PAM, and the solution of kinematic parameters is optimized by Random Sample Consensus (RANSAC). Third, the uncertainty of kinematic calibration is analyzed by Monte Carlo method. Finally, a simulated posture adjustment system was built to calibrate the kinematics parameters of PAM, and the posture adjustment experiment was carried out according to the calibration results.

The experiment results show that the proposed calibration method can significantly improve the posture adjustment accuracy and greatly reduce the posture adjustment stress.

In this paper, a calibration method based on force feedback is proposed to avoid the deformation of NCL and bracket caused by redundant driving during the calibration process, and RANSAC method is used to reduce the influence of large random error on the calibration accuracy.

This paper aims to propose a measurement principle and a calibration method of measurement system integrated with serial robot and 3D camera to identify its parameters conveniently and achieve high measurement accuracy.

A stiffness and kinematic measurement principle of the integrated system is proposed, which considers the influence of robot weight and load weight on measurement accuracy. Then an error model is derived based on the principle that the coordinate of sphere center is invariant, which can simultaneously identify the parameters of joint stiffness, kinematic and hand-eye relationship. Further, considering the errors of the parameters to be calibrated and the measurement error of 3D camera, a method to generate calibration observation data is proposed to validate both calibration accuracy and parameter identification accuracy of calibration method.

Comparative simulations and experiments of conventional kinematic calibration method and the stiffness and kinematic calibration method proposed in this paper are conducted. The results of the simulations show that the proposed method is more accurate, and the identified values of angle parameters in modified Denavit and Hartenberg model are closer to their real values. Compared with the conventional calibration method in experiments, the proposed method decreases the maximum and mean errors by 19.9% and 13.4%, respectively.

A new measurement principle and a novel calibration method are proposed. The proposed method can simultaneously identify joint stiffness, kinematic and hand-eye parameters and obtain not only higher measurement accuracy but also higher parameter identification accuracy, which is suitable for on-site calibration.

This paper aims to present an approach for calibrating the numerical models of dynamical systems that have spatially localized nonlinear components. The approach implements the extended constitutive relation error (ECRE) method using multi-harmonic coefficients and is conceived to separate the errors in the representation of the global, linear and local, nonlinear components of the dynamical system through a two-step process.

The first step focuses on the system’s predominantly linear dynamic response under a low magnitude periodic excitation. In this step, the discrepancy between measured and predicted multi-harmonic coefficients is calculated in terms of residual energy. This residual energy is in turn used to spatially locate errors in the model, through which one can identify the erroneous model inputs which govern the linear behavior that need to be calibrated. The second step involves measuring the system’s nonlinear dynamic response under a high magnitude periodic excitation. In this step, the response measurements under both low and high magnitude excitation are used to iteratively calibrate the identified linear and nonlinear input parameters.

When model error is present in both linear and nonlinear components, the proposed iterative combined multi-harmonic balance method (MHB)-ECRE calibration approach has shown superiority to the conventional MHB-ECRE method, while providing more reliable calibration results of the nonlinear parameter with less dependency on a priori knowledge of the associated linear system.

This two-step process is advantageous as it reduces the confounding effects of the uncertain model parameters associated with the linear and locally nonlinear components of the system.

This research paper aims to optimize the calibration process for an ABB IRB 120 robot, specifically for robotic orbital milling applications, by introducing and validating the use of the observability index and telescopic ballbar for accuracy enhancement.

The study uses the telescopic ballbar and an observability index for the calibration of an ABB IRB 120 robot, focusing on robotic orbital milling. Comparative simulation analysis selects the O₃ index. Experimental tests, both static and dynamic, evaluate the proposed calibration approach within the robot’s workspace.

The proposed calibration approach significantly reduces circularity errors, particularly in robotic orbital milling, showcasing effectiveness in both static and dynamic modes at various tool center point speeds.

The study focuses on a specific robot model and application (robotic orbital milling), limiting generalizability. Further research could explore diverse robot models and applications.

The findings offer practical benefits by enhancing the accuracy of robotic systems, particularly in precision tasks like orbital milling, providing a valuable calibration method.

While primarily technological, improved robotic precision can have social implications, potentially influencing fields where robotic applications are crucial, such as manufacturing and automation.

This study’s distinctiveness lies in advancing the accuracy and precision of industrial robots during circular motions, specifically tailored for orbital milling applications. The innovative approach synergistically uses the observability index and telescopic ballbar to achieve these objectives.

The purpose of this paper is to describe a calibration method developed to improve the accuracy of a six degrees-of-freedom medical robot. The proposed calibration approach aims to enhance the robot’s accuracy in a specific target workspace. A comparison of five observability indices is also done to choose the most appropriate calibration robot configurations.

The calibration method is based on the forward kinematic approach, which uses a nonlinear optimization model. The used experimental data are 84 end-effector positions, which are measured using a laser tracker. The calibration configurations are chosen through an observability analysis, while the validation after calibration is carried out in 336 positions within the target workspace.

Simulations allowed finding the most appropriate observability index for choosing the optimal calibration configurations. They also showed the ability of our calibration model to identify most of the considered robot’s parameters, despite measurement errors. Experimental tests confirmed the simulation findings and showed that the robot’s mean position error is reduced from 3.992 mm before calibration to 0.387 mm after, and the maximum error is reduced from 5.957 to 0.851 mm.

This paper presents a calibration method which makes it possible to accurately identify the kinematic errors for a novel medical robot. In addition, this paper presents a comparison between the five observability indices proposed in the literature. The proposed method might be applied to any industrial or medical robot similar to the robot studied in this paper.

This paper aims to propose a united kinematic calibration method for a dual-machine system in automatic drilling and riveting. The method takes both absolute and relative pose accuracy into account, which will largely influence the machining accuracy of the dual-machine system and assembly quality.

A comprehensive kinematic model of the dual-machine system is established by the superposition of sub-models with pose constraints, which involves base frame parameters, kinematic parameters and tool frame parameters. Based on the kinematic model and the actual pose error data measured by a laser tracker, the parameters of coordinated machines are identified by the Levenberg–Marquardt method as a multi-objective nonlinear optimization problem. The identified parameters of the coordinated machines will be used in the control system.

A new calibration method for the dual-machine system is developed, including a comprehensive kinematic model and an efficient parameter identification method. The experiment results show that with the proposed method, the pose accuracy of the dual-machine system was remarkably improved, especially the relative position and orientation errors.

This method has been used in an aircraft assembly project. The calibrated dual-machine system shows a good performance on system coordination and machining accuracy.

This paper proposes a new method with high accuracy and efficiency for the dual-machine system calibration. The research can be extended to multi-machine and multi-robot fields to improve the system precision.

The purpose of this paper is to develop a means of the kinematic calibration of a parallel manipulator with full-circle rotation.

An error-mapping model based on the space vector chain is formulated and parameter identification is proposed based on double ball-bar (DBB) measurements. The measurement trajectory is determined by the motion characteristics of this mechanism and whether the error sources can be identified. Error compensation is proposed by modifying the inputs, and a two-step kinematic calibration method is implemented.

The simulation and experiment results show that this kinematic calibration method is effective. The DBB length errors and the position errors in the end-effector of the parallel manipulator with full-circle rotation are greatly reduced after error compensation.

By establishing the mapping relationship between measured error data and geometric error sources, the error parameters of this mechanism are identified; thus, the pose errors are unnecessary to be measured directly. The effectiveness of the kinematic calibration method is verified by computer simulation and experiment. This proposed calibration method can help the novel parallel manipulator with full-circle rotation and other similar parallel mechanisms to improve their accuracy.