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The purpose of this paper is to present a distance accuracy-based industrial robot kinematic calibration model. Nowadays, the repeatability of the industrial robot is high, while the absolute positioning accuracy and distance accuracy are low. Many factors affect the absolute positioning accuracy and distance accuracy, and the calibration method of the industrial robot is an important factor. When the traditional calibration methods are applied on the industrial robot, the accumulative error will be involved according to the transformation between the measurement coordinate and the robot base coordinate.

In this manuscript, a distance accuracy-based industrial robot kinematic calibration model is proposed. First, a simplified kinematic model of the robot by using the modified Denavit–Hartenberg (MDH) method is introduced, then the proposed distance error-based calibration model is presented; the experiment is set up in the next section.

The experimental results show that the proposed calibration model based on MDH and distance error can improve the distance accuracy and absolute position accuracy dramatically.

The proposed calibration model based on MDH and distance error can improve the distance accuracy and absolute position accuracy dramatically.

Intelligent and connected vehicle technology is in the ascendant. High-level autonomous driving places more stringent requirements on the accuracy and reliability of environmental perception. Existing research works on multitarget tracking based on multisensor fusion mostly focuses on the vehicle perspective, but limited by the principal defects of the vehicle sensor platform, it is difficult to comprehensively and accurately describe the surrounding environment information.

In this paper, a multitarget tracking method based on roadside multisensor fusion is proposed, including a multisensor fusion method based on measurement noise adaptive Kalman filtering, a global nearest neighbor data association method based on adaptive tracking gate, and a Track life cycle management method based on M/N logic rules.

Compared with fixed-size tracking gates, the adaptive tracking gates proposed in this paper can comprehensively improve the data association performance in the multitarget tracking process. Compared with single sensor measurement, the proposed method improves the position estimation accuracy by 13.5% and the velocity estimation accuracy by 22.2%. Compared with the control method, the proposed method improves the position estimation accuracy by 23.8% and the velocity estimation accuracy by 8.9%.

A multisensor fusion method with adaptive Kalman filtering of measurement noise is proposed to realize the adaptive adjustment of measurement noise. A global nearest neighbor data association method based on adaptive tracking gate is proposed to realize the adaptive adjustment of the tracking gate.

Aircraft structures are mainly connected by riveting joints, whose quality and mechanical performance are directly determined by vertical accuracy of riveting holes. This paper proposed a combined vertical accuracy compensation method for drilling and riveting of aircraft panels with great variable curvatures.

The vertical accuracy compensation method combines online and offline compensation categories in a robot riveting and drilling system. The former category based on laser ranging is aimed to correct the vertical error between actual and theoretical riveting positions, and the latter based on model curvature is used to correct the vertical error caused by the approximate plane fitting in variable-curvature panels.

The vertical accuracy compensation method is applied in an automatic robot drilling and riveting system. The result reveals that the vertical accuracy error of drilling and riveting is within 0.4°, which meets the requirements of the vertical accuracy in aircraft assembly.

The proposed method is suitable for improving the vertical accuracy of drilling and riveting on panels or skins of aerospace products with great variable curvatures without introducing extra measuring sensors.

For decades, the Department of Defense (DoD) has employed numerous reporting and monitoring tools for characterizing the acquisition cost estimates of its programs. These tools have led to dozens of studies thoroughly documenting the magnitude and extent of DoD acquisition cost growth. However, little attention has been paid to the behavior of the other main cost component of a system's life cycle cost: Operating and Support (O&S) costs. Consequently, the DoD has little knowledge regarding the accuracy of O&S cost estimates or how that accuracy changes over time. In a previous paper, the authors described an analytical methodology for remedying this deficiency via a study to characterize the historical accuracy of O&S cost estimates. The results are presented here, and indicate there tend to be large errors in DoD O&S cost estimates, and that the accuracy of the estimates improves little over time

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.

This paper aims to extend the research into company financial forecasts by modelling naïve earnings forecasts derived from normalised historic accounting data disclosed during Australian initial public offerings (IPOs). It seeks to investigate naïve forecast errors and compare them against their management forecast counterparts. It also seeks to investigate determinants of differential error behaviour.

IPOs were sampled and their prospectus forecasts, historic financial data and subsequent actual financial performance were analysed. Directional and absolute forecast error behaviour was analysed using univariate and multivariate techniques.

Systematic factors associated with error behaviour were observed across the management forecasts and the naïve forecasts, the most notable being audit quality. In certain circumstances, the naïve forecasts performed at least as well as management forecasts. In particular, forecast interval was an important discriminator for accuracy, with the superiority of management forecasts only observed for shorter forecast intervals.

The results imply a level of “disclosure management” regarding company IPO forecasts and normalised historic accounting data, with forecast overestimation and error size more extreme in the absence of higher quality third‐party monitoring services via the audit process. The results also raise questions regarding the serviceability of normalised historic financial information disclosed in prospectuses, in that many of those data do not appear to enhance the forecasting process, particularly when accompanied by published management forecasts and shorter forecast intervals.

In order to save cost and time, attempts are made by the automotive industry to simulate in advance system planning and programming of robots involved in the manufacturing process. Unfortunately, the use of off‐line programming produces inaccuracies in robot positioning so that reteaching is required for the various working points. This problem is aggravated by the fact that to date robot manufacturers have been unable to provide reliable data on the accuracy of their robots when using off‐line programming. This complex of problems is discussed on the basis of the VDI directive 2861.

This paper aims to present a complete relative pose error model for robot calibration, considering both the relative distance error and the relative rotation error of the robot end-effector, which can improve calibration accuracy.

In this paper, the relative distance error model and the relative rotation error model of robot calibration are derived by ignoring high-order nonlinear errors, and the two models form into a complete relative pose error model. Besides, mathematical expectation of the nonlinear errors is calculated, indicating that they have little influence on calibration accuracy.

Comparative experiments have indicated that the proposed complete relative pose error model does better in robot calibration than only the distance error model.

The main contribution of this paper lies in the derivation of the relative rotation error model, which helps to form a complete relative pose error model for calibration. The proposed method improves calibration accuracy, with avoiding identifying the transformation matrix between the measurement system frame and the robot base frame.

Economic, flexible and efficient micro production needs new miniaturized automation equipment (desktop factories). Micro assembly processes make demands on precision of miniaturized robots used in desktop factories and the driving concepts, as well as miniaturized machine elements. The purpose of this paper is to investigate miniaturized drives using micro harmonic drive gears, which are promising driving concepts.

The analysis of the miniaturized precision robot Parvus (using micro harmonic drive gears) shows a good repeatability but also room for improvement concerning the path accuracy. Thereby the transmission error of the micro gears is identified as main disturbing influence concerning the robot's precision characteristics. Owing to the size reduction of the micro harmonic drive gear and the slightly different working principle compared to larger harmonic drive gears, the transmission error are more pronounced. Therefore, it is necessary to discuss approaches to compensate for this effect.

A very promising approach is the use of a simplified model of the kinematic error within the robot control to compensate for this disturbing effect. Measurement data of the transmission error is mathematically transformed into the frequency domain and filtered to the most important frequency modes of the function. These modes are used to build up a simplified mathematic model of the gear transmission error. A final test using this model as compensation function demonstrates that it is possible to reduce the transmission error of the micro gears by more than 50 percent.

The paper presents the first investigation into compensation of the transmission error of micro harmonic drive gears.