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This paper aims to improve calibration and force measurement accuracy of multi-sensors’ piezoelectric dynamometer used in thrust measurement of rocket/air vehicle engine.

This paper presents a mapping solution method of sensors’ outputs based on the Kirchhoff thin plate theory, builds force-deformation differential equations with specific boundary conditions, uses finite difference (FD) method to solve the equations and analyzes outputs in offset loading forces in four-sensor square layout in main direction. The resultant force deviations calculated by the Kirchhoff theory are optimized with sequence quadratic program (SQP) method, and a calibration method of multiple loading points (MLP) based on the Kirchhoff theory is presented. Experiments of static calibration and verification are complemented to contrast the novel and single loading point (SLP) calibration method.

Experiments of static calibration and its verification show that at a loading force of 5,000N, the average resultant force deviations with MLP is 17.87N (0.35% FS) compared with single loading point method 26.45N (0.53% FS), improving calibration and measurement precision.

A novel calibration method with MLP is presented. Force distributions of multiple sensors of main direction in piezoelectric dynamometer with offset loading force are solved with the Kirchhoff theory. The resultant force deviations calculated by Kirchhoff theory are optimized with the SQP method.

This paper aims to verify a new method for accurate part manufacturing using a 3D printer. In particular, the direction and position dependence of the printed results are to be verified within the building area. The results of the accomplished experiments are to be used for the computation of new printer adjustments.

Test cubes with a defined edge length were printed and measured afterwards. The test cubes were distributed thereby either over the entire building area or only for a small part of the building area. Next, the test cubes were measured and the differences between measured and desired values were used for adjustment of the printer parameter settings. Therefore, the “bleed compensation” settings were used.

The deviations depended strongly on the position in the building area of the printer. In dependence of the position and orientation, different deviations in the three dimensions of the printer coordinate system resulted. By a calibration of the printer parameters for a reduced part of the processed area, the print accuracy could be strongly increased. Afterward, the calibration the deviations could be reduced from 0.4 mm ±0.2 mm to under 0.04 mm ±0.03 mm.

The work shows the position and direction dependency of the 3D‐printer manufacturing accuracy. Furthermore, a calibration procedure for bleed compensation calibration is presented.

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.

The purpose of this paper is to propose a robot-assisted assembly system (RAAS) for the installation of a variety of small components in the aircraft assembly system. The RAAS is designed to improve the assembly accuracy and increase the productive efficiency.

The RAAS is a closed-loop feedback system, which is integrated with a laser tracking system and an industrial robot system. The laser tracking system is used to evaluate the deviations of the position and orientation of the small component and the industrial robot system is used to locate and re-align the small component according to the deviations.

The RAAS has exhibited considerable accuracy improvement and acceptable assembly efficiency in aircraft assembly project. With the RAAS, the maximum position deviation of the component is reduced to 0.069 mm and the maximum orientation deviation is reduced to 0.013°.

The RAAS is applied successfully in one of the aircraft final assembly projects in southwest China.

By integrating the laser tracking system, the RAAS is constructed as a closed-loop feedback system of both the position and orientation of the component. With the RAAS, the installation a variety of small components can be dealt with by a single industrial robot.

2D laser rangefinders (LRFs) are commonly used sensors in the field of robotics, as they provide accurate range measurements with high angular resolution. These sensors can be coupled with mechanical units which, by granting an additional degree of freedom to the movement of the LRF, enable the 3D perception of a scene. To be successful, this reconstruction procedure requires to evaluate with high accuracy the extrinsic transformation between the LRF and the motorized system.

In this work, a calibration procedure is proposed to evaluate this transformation. The method does not require a predefined marker (commonly used despite its numerous disadvantages), as it uses planar features in the point acquired clouds.

Qualitative inspections show that the proposed method reduces artifacts significantly, which typically appear in point clouds because of inaccurate calibrations. Furthermore, quantitative results and comparisons with a high-resolution 3D scanner demonstrate that the calibrated point cloud represents the geometries present in the scene with much higher accuracy than with the un-calibrated point cloud.

The last key point of this work is the comparison of two laser scanners: the lemonbot (authors’) and a commercial FARO scanner. Despite being almost ten times cheaper, the laser scanner was able to achieve similar results in terms of geometric accuracy.

This work describes a novel calibration technique that is easy to implement and is able to achieve accurate results. One of its key features is the use of planes to calibrate the extrinsic transformation.

The purpose of this paper is to propose a method that calibrates the hand-eye relationship for eye-to-hand configuration and afterwards a rectification to improve the accuracy of general calibration.

The hand-eye calibration of eye-to-hand configuration is summarized as a equation AX = XB which is the same as in eye-in-hand calibration. A closed-form solution is derived. To abate the impact of noise, a rectification is conducted after the general calibration.

Simulation and actual experiments confirm that the accuracy of calibration is obviously improved.

Only a calibration plane is required for the hand-eye calibration. Taking the impact of noise into account, a rectification is carried out after the general calibration and, as a result, that the accuracy is obviously improved. The method can be applied in many actual applications.

In a dual-robot system, the relative position error is a superposition of errors from each mono-robot, resulting in deteriorated coordination accuracy. This study aims to enhance relative accuracy of the dual-robot system through direct compensation of relative errors. To achieve this, a novel calibration-driven transfer learning method is proposed for relative error prediction in dual-robot systems.

A novel local product of exponential (POE) model with minimal parameters is proposed for error modeling. And a two-step method is presented to identify both geometric and nongeometric parameters for the mono-robots. Using the identified parameters, two calibrated models are established and combined as one dual-robot model, generating error data between the nominal and calibrated models’ outputs. Subsequently, the calibration-driven transfer, involving pretraining a neural network with sufficient generated error data and fine-tuning with a small measured data set, is introduced, enabling knowledge transfer and thereby obtaining a high-precision relative error predictor.

Experimental validation is conducted, and the results demonstrate that the proposed method has reduced the maximum and average relative errors by 45.1% and 30.6% compared with the calibrated model, yielding the values of 0.594 mm and 0.255 mm, respectively.

First, the proposed calibration-driven transfer method innovatively adopts the calibrated model as a data generator to address the issue of real data scarcity. It achieves high-accuracy relative error prediction with only a small measured data set, significantly enhancing error compensation efficiency. Second, the proposed local POE model achieves model minimality without the need for complex redundant parameter partitioning operations, ensuring stability and robustness in parameter identification.

The purpose of this paper is to propose an on-line iterative compensation method combining with a feed-forward compensation method to enhance the assembly accuracy of a metrology-integrated robot system (MIRS).

By the integration of a six degrees of freedom (6DoF) measurement system (T-Mac), the robot’ movement can be tracked with real-time measurement. With the on-line measured data, the proposed iterative compensation for absolute positioning and the feed-forward compensation for relative linear motion are integrated into the assembly process to improve the assembly accuracy.

It is found that the MIRS exhibits good performance in both accuracy and efficiency with the application of the proposed compensation method. With the proposed assembly process, a component can be automatically aligned to the target in seconds, and the assembly error can be decreased to 0.021 mm for position and 0.008° for orientation on average.

This paper presents a 6DoF MIRS for high-precision assembly. Based on the system, a novel on-line compensation method is proposed to enhance the assembly accuracy. In this paper, the assembly accuracy and the corresponding distance parameter are given by a series of experiments as reference for assembly applications.

The purpose of this study is to use visual and inertial sensors to achieve real-time location. How to provide an accurate location has become a popular research topic in the field of indoor navigation. Although the complementarity of vision and inertia has been widely applied in indoor navigation, many problems remain, such as inertial sensor deviation calibration, unsynchronized visual and inertial data acquisition and large amount of stored data.

First, this study demonstrates that the vanishing point (VP) evaluation function improves the precision of extraction, and the nearest ground corner point (NGCP) of the adjacent frame is estimated by pre-integrating the inertial sensor. The Sequential Similarity Detection Algorithm (SSDA) and Random Sample Consensus (RANSAC) algorithms are adopted to accurately match the adjacent NGCP in the estimated region of interest. Second, the model of visual pose is established by using the parameters of the camera itself, VP and NGCP. The model of inertial pose is established by pre-integrating. Third, location is calculated by fusing the model of vision and inertia.

In this paper, a novel method is proposed to fuse visual and inertial sensor to locate indoor environment. The authors describe the building of an embedded hardware platform to the best of their knowledge and compare the result with a mature method and POSAV310.

This paper proposes a VP evaluation function that is used to extract the most advantages in the intersection of a plurality of parallel lines. To improve the extraction speed of adjacent frame, the authors first proposed fusing the NGCP of the current frame and the calibrated pre-integration to estimate the NGCP of the next frame. The visual pose model was established using extinction VP and NGCP, calibration of inertial sensor. This theory offers the linear processing equation of gyroscope and accelerometer by the model of visual and inertial pose.

Outlines the development of a robot measurement system, ICAROS, which combines the advantages of photogrammetrical and inertial measurement procedures, allowing the acquisition of measuring values for the determination of all [dynamic and static] industrial robot performance parameters, according to ISO 9283. Describes the photogrammetrical component and inertial system component of the pose measuring system and the integration of the two parts and looks at the over‐all‐calibration procedure in Off‐line and on‐line phase. Concludes that the system allows the pose measurement and calibration of robots, the scanning of robot programs and the transmission of these programs to other robot systems.