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The air data system of an aircraft includes, amongst other items, the airspeed indicator, altimeter and machmeter all of which derive their readings from measurement of air pressures. The instruments are designed on the assumption that they will be fed with pressures from the undisturbed free‐stream in which the aircraft is flying; this is not generally the case. In flight the aircraft disturbs the air mass and in doing so causes a pressure field around itself which produces the forces necessary for lift and control. The pressure sensors which detect the airstream pressures may be located within the aircraft pressure field and the pressures transmitted to the instruments may not correspond to the free‐stream pressures; if this is so then the instrument indications will be in error.

Laboratory calibration methods are time-consuming and require accurate devices to find the error coefficients of the low-cost microelectromechanical system (MEMS) accelerometer. Besides, low-cost MEMS sensors highly depend on temperature because of their silicon property and the effect of temperature on error coefficients should also be considered for compensation. This paper aims to present a field calibration method in which the accelerometer is placed in different positions without any accurate equipment in a few minutes and its temperature is changed by a simple device like a hairdryer.

In this paper, a non-linear cost function is defined based on this rule that the magnitude of the acceleration measured by the accelerometer in static mode is equal to the gravity plus error factors. Also, the dependency of error coefficients of the accelerometer is presented as a second-order polynomial in this cost function. By minimizing the cost function, the accelerometer error coefficients include bias, scale factor and non-orthogonality and their temperature dependency are obtained simultaneously.

Simulation results in MATLAB and empirical results of a MPU6050 accelerometer verify the good performance of the proposed calibration method.

Finding a fast and simple field calibration method to calibrate a low-cost MEMS accelerometer and compensate for the temperature dependency without using accurate laboratory equipment can help a wide range of industries that use advanced and expensive sensors or use expensive laboratory equipment to calibrate their sensors, to decrease their costs.

We have succeeded in capturing porcine 3D surface anatomy in vivo by developing a high‐resolution stereo imaging system. The system achieved accurate 3D shape recovery by matching stereo pair images containing only natural surface textures at high (image) resolution. The 3D imaging system presented for pig shape capture is based on photogrammetry and comprises: stereo pair image acquisition, stereo camera calibration and stereo matching and surface and texture integration. Practical issues have been addressed, and in particular the integration of multiple range images into a single 3D surface. Robust image segmentation successfully isolated the pigs within the stereo images and was employed in conjunction with depth discontinuity detection to facilitate the integration process. The capture and processing chain is detailed here and the resulting 3D pig anatomy obtained using the system presented.

Bench‐top wind tunnels are used extensively by the US Air Force for calibrating anemometers. As anemometers have improved, the need for reduced uncertainties in the bench‐top wind tunnels was required. A three‐pronged approach was used to reduce low velocity uncertainties by a factor of 2‐3.Design/methodology/approach – The reduction in velocity uncertainties was achieved by upgrading the wind tunnel instrumentation that measured the pressure and differential pressure and by improving the velocity calibration of the bench‐top wind tunnel. A detailed uncertainty analysis was performed to determine how much the instrumentation needed to improve. A laser Doppler velocimetry (LDV) was used to calibrate each wind tunnel at low velocities.Findings – The uncertainty analysis indicated that the main contributors to the velocity uncertainty were the differential pressure and the pressure measurements. These two process instruments were upgraded to reduce their individual uncertainties by a factor of 2. Additionally each bench‐top wind tunnel was calibrated using the LDV with special emphasis on flows from 0.15‐3.0 m/s. In all, nine wind tunnels were calibrated and the upgraded systems exhibited a reduction in uncertainties in the low flow region of a factor of 2‐3.Originality/value – A need to reduce velocity uncertainties in bench‐top wind tunnels was a requirement for the US Air Force calibration program. Upgraded instrumentation and individual calibration with an LDV provided the needed reduction. In the low flow region of 0.15 to 3.0 m/s, uncertainties were reduced by a factor of 2‐3.

Recognition and guidance of initial welding position (IWP) is one of the most important steps of automatic welding process, also a key technology of autonomous welding process. The purpose of this paper is to advance an improved Harris Algorithm and grey scale scanning method (GSCM) to raise the precision of image processing.

Through the configuration of “single camera and double positions,” a new set of image processing algorithms is adopted to extract feature points by using the pattern of rough location and subtle extraction, so as to restructure three‐dimensional information to guide robot move to IWP in the practical welding environment.

Experiments showed that mean square errors (MSEs) in X, Y, Z‐directions for both flat butt joint and flat flange are 0.4491, 0.8178, 1.4797, and 0.5398, 0.4861, 1.1071 mm, respectively.

It has a limitation in providing guidance for only one step, and would be more accurate if fractional steps are adopted.

Guidance experiments of IWPs on oxidant tank's simulating parts are carried out, whose success rate is up to 95 percent and MSEs are 0.7407, 0.7971, and 1.3429 mm. It meets the demands of continuous and automatic welding process.

Improved Harris Algorithm and GSCM are advanced to raise the precision of image processing which influenced guidance precision most.

Many research efforts have turned to sensing, and in particular computer vision, to create more flexible robotic systems. Computer vision is often required to provide data for the grasping of a target. Using a vision system for grasping of static or moving objects presents several issues with respect to sensing, control, and system configuration. This paper presents some of these issues in concept with the options available to the researcher and the trade‐offs to be expected when integrating a vision system with a robotic system for the purpose of grasping objects. The paper includes a description of our experimental system and contains experimental results from a particular configuration that characterize the type and frequency of errors encountered while performing various vision‐guided grasping tasks. These error classes and their frequency of occurrence lend insight into the problems encountered during visual grasping and into the possible solution of these problems.

The purpose of this paper is to improve the reliability of the force measurement system by determining the reliable test range of dynamometer.

Based on the principle of leverage and moment balance, a general force distribution model is applicable in where the test point is located either inside or outside the support region of four three-component force links of dynamometer is established. After corroborating the correctness of the model through verification experiments, the boundary conditions that each three-component force link should satisfy are analyzed by considering the characteristic of the dynamometer components comprehensively. Furthermore, the reliable test range of dynamometer is determined, followed by a calibration experiment to verify its rationality.

The relationships between the reliable test range and the tested force, the bolt pre-tightening force and the bearing capacity of quartz wafers are clarified. Further, the experimental calibration results show that when the test point is within the reliable test range, the three-directional output voltage of dynamometer has excellent linearity and repeatability. The nonlinearity and repeatability in X-, Y- and Z-directions are all less than 1.1%.

A general mathematical model of force distribution of four three-component force links is constructed, which provides a theoretical basic for the mechanical analysis of multi-sensors’ dynamometer. Comprehensively considering the performance of dynamometer components, the value of measured force and the pre-tightening force, the simultaneous equations of reliable test range are deduced, which limits the boundary of allowable test position of piezoelectric dynamometer.

This paper discusses a novel assembly method and system for a commercially available 8 mm diameter miniature planetary gearhead. Our system comprises a commercially available four‐degree of freedom industrial robot, two vision systems, a force feedback system in the robot wrist, and specially designed flexible part feeders. The system has proved successful in assembling planetary gear units independently. Depending on the task, one can select whether to use the high accuracy and repeatability of the robot or alternatively use programmable frequency vibration in the gripper to stochastically align the parts that the robot handles.

In order to bridge the increasing gap between the micro‐ and nanotechnologies, a European consortium is currently developing and investigating a cluster of mobile, wireless cubic centimetre‐sized microrobots. The control and sensor issues which are to be solved for such a robot system are demanding. This paper describes the work carried out by one of the project partners. An interferometrical principle employing the so‐called “mechanical” interferometer based on the Moiré‐effect is used for the position sensor system. Further sensor systems involve “local” microscope cameras, for which the extraction of depth information is crucial.