scholarly journals Calibration Techniques For Low-Cost Star Trackers

2021 ◽  
Author(s):  
Tomas Dzamba
Keyword(s):  
Low Cost ◽  
Gaussian Model ◽  
Geometric Calibration ◽  
Ground Testing ◽  
Sensor Performance ◽  
Field Curvature ◽  
Image Shape ◽  
Calibration Techniques ◽  
Optical Calibration ◽  
Matching Techniques

This study presents a series of cost-effect strategies for calibrating star trackers for microsatellite missions. We examine three such strategies that focus on the calibration of the image detector, geometric calibration of the lab setup used for ground testing, and an optical calibration due to lens aberrations. Procedures are developed for each of these strategies that emphasize speed of implementation and accuracy, while trying to minimize manual labour. For the detector calibration, an existing calibration technique was adapted and implemented to reduce fixed pattern noise and dark current. Preliminary results show reduced variations in pixel sensitivity by approximately 21%, averaged across each pixel color given the use of a color imager. Although not substantial, this reduction in pixel variation will help preserve the Gaussian illumination pattern of imaged stars, aiding in correct centroid location. Results pertaining to the lab calibration show accurate star placement, in angular terms to 0.0073º across most of the field of view. This provides an accurate low-cost, variable solution for characterizing sensor performance; specifically pattern matching techniques. Finally, we present some initial results for lens aberration characterization. Using a Gaussian model of star image shape gives trends consistence with astigmatism and field curvature aberrations. Together, these calibrations represent tools that aim to improve both development and manufacture of modern microsatellite star trackers.

scholarly journals Calibration Techniques For Low-Cost Star Trackers

2021 ◽  
Author(s):  
Tomas Dzamba
Keyword(s):  
Low Cost ◽  
Gaussian Model ◽  
Geometric Calibration ◽  
Ground Testing ◽  
Sensor Performance ◽  
Field Curvature ◽  
Image Shape ◽  
Calibration Techniques ◽  
Optical Calibration ◽  
Matching Techniques

This study presents a series of cost-effect strategies for calibrating star trackers for microsatellite missions. We examine three such strategies that focus on the calibration of the image detector, geometric calibration of the lab setup used for ground testing, and an optical calibration due to lens aberrations. Procedures are developed for each of these strategies that emphasize speed of implementation and accuracy, while trying to minimize manual labour. For the detector calibration, an existing calibration technique was adapted and implemented to reduce fixed pattern noise and dark current. Preliminary results show reduced variations in pixel sensitivity by approximately 21%, averaged across each pixel color given the use of a color imager. Although not substantial, this reduction in pixel variation will help preserve the Gaussian illumination pattern of imaged stars, aiding in correct centroid location. Results pertaining to the lab calibration show accurate star placement, in angular terms to 0.0073º across most of the field of view. This provides an accurate low-cost, variable solution for characterizing sensor performance; specifically pattern matching techniques. Finally, we present some initial results for lens aberration characterization. Using a Gaussian model of star image shape gives trends consistence with astigmatism and field curvature aberrations. Together, these calibrations represent tools that aim to improve both development and manufacture of modern microsatellite star trackers.

scholarly journals ACCURACY ASSESSMENT AND CALIBRATION OF LOW-COST AUTONOMOUS LIDAR SENSORS

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 371-376
Author(s):  
C. L. Glennie ◽  
P. J. Hartzell
Keyword(s):  
Autonomous Vehicles ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Point Clouds ◽  
Geometric Calibration ◽  
Sensor Performance ◽  
Least Squares Adjustment ◽  
The Individual ◽  
Small Form Factor ◽  
Surface Constraints

Abstract. A number of low-cost, small form factor, high resolution lidar sensors have recently been commercialized in an effort to fill the growing needs for lidar sensors on autonomous vehicles. These lidar sensors often report performance as range precision and angular accuracy, which are insufficient to characterize the overall quality of the point clouds returned by these sensors. Herein, a detailed geometric accuracy analysis of two representative autonomous sensors, the Ouster OSI-64 and the Livox Mid-40, is presented. The scanners were analyzed through a rigorous least squares adjustment of data from the two sensors using planar surface constraints. The analysis attempts to elucidate the overall point cloud accuracy and presence of systematic errors for the sensors over medium (< 40 m) ranges. The Livox Mid-40 sensor performance appears to be in conformance with the product specifications, with a ranging accuracy of approximately 2 cm. No significant systematic geometric errors were found in the acquired Mid-40 point clouds. The Ouster OSI-64 did not perform to the manufacturer specifications, with a ranging accuracy of 5.6 cm, which is nearly twice that stated by the manufacturer. Several of the individual lasers within the OSI-64’s bank of 64 lasers exhibited higher range noise than their counterparts, and examination of the residuals indicate a possible systematic error correlated with the horizontal encoder angle. This suggests that the Ouster laser may benefit from additional geometric calibration. Finally, both sensors suffered from an inability to accurately resolve edges and smaller features such as posts due to their large laser beam divergences.

A Multimodule Planar Air Bearing Testbed for CubeSat-Scale Spacecraft

2013 ◽  
Vol 135 (4) ◽  
Author(s):  
William R. Wilson ◽  
Laura L. Jones ◽  
Mason A. Peck
Keyword(s):  
Multibody Dynamics ◽  
Technology Development ◽  
Low Cost ◽  
Air Bearing ◽  
Cost Performance ◽  
Small Satellites ◽  
Ground Testing ◽  
Spacecraft Mission ◽  
Cost Scaling ◽  
Design Cost

In the past several years, small satellites have taken on an increasingly important role as affordable technology demonstrators and are now being viewed as viable low-cost platforms for traditional spacecraft mission objectives. As such, the CubeSat standard (1 kg in a 10 cm cube) has been widely adopted for university-led development efforts even as it is embraced by traditional spacecraft developers, such as NASA. As CubeSats begin to take on roles traditionally filled by much larger spacecraft, the infrastructure for dynamics and controls testing must also transition to accommodate the different size and cost scaling associated with CubeSats. While air-bearing-based testbeds are commonly used to enable a variety of traditional ground testing and development for spacecraft, few existing designs are suitable for development of CubeSat-scale technologies, particularly involving multibody dynamics. This work describes Cornell University's FloatCube testbed, which provides a planar reduced-friction environment for multibody dynamics and controls technology development for spacecraft less than 6 kg and a 15 cm cube. The multimodule testbed consists of four free-floating air-bearing platforms with on-board gas supplies that allow the platforms to float over a glass surface without external attachments. Each of these platforms, or FloatCubes, can host CubeSat-sized payloads at widely ranging levels of development, from prototype components to full-scale systems. The FloatCube testbed has already hosted several successful experiments, proving its ability to provide an affordable reduced-friction environment to CubeSat-scale projects. This paper provides information on the system design, cost, performance, operating procedures, and applications of this unique, and increasingly relevant, testbed.

scholarly journals Community Air Sensor Network (CAIRSENSE) project: Evaluation of low-cost sensor performance in a suburban environment in the southeastern United States

2016 ◽  
Author(s):  
Wan Jiao ◽  
Gayle Hagler ◽  
Ronald Williams ◽  
Robert Sharpe ◽  
Ryan Brown ◽  
...  
Keyword(s):  
Air Pollution ◽  
Carbon Monoxide ◽  
Air Quality ◽  
Sulfur Dioxide ◽  
Sensor Network ◽  
Correlation Coefficient ◽  
Low Cost ◽  
Ambient Air ◽  
Sensor Technology ◽  
Sensor Performance

Abstract. Advances in air pollution sensor technology have enabled the development of small and low cost systems to measure outdoor air pollution. The deployment of a large number of sensors across a small geographic area would have potential benefits to supplement traditional monitoring networks with additional geographic and temporal measurement resolution, if the data quality were sufficient. To understand the capability of emerging air sensor technology, the Community Air Sensor Network (CAIRSENSE) project deployed low cost, continuous and commercially-available air pollution sensors at a regulatory air monitoring site and as a local sensor network over a surrounding ~ 2 km area in Southeastern U.S. Co-location of sensors measuring oxides of nitrogen, ozone, carbon monoxide, sulfur dioxide, and particles revealed highly variable performance, both in terms of comparison to a reference monitor as well as whether multiple identical sensors reproduced the same signal. Multiple ozone, nitrogen dioxide, and carbon monoxide sensors revealed low to very high correlation with a reference monitor, with Pearson sample correlation coefficient (r) ranging from 0.39 to 0.97, −0.25 to 0.76, −0.40 to 0.82, respectively. The only sulfur dioxide sensor tested revealed no correlation (r  0.5), step-wise multiple linear regression was performed to determine if ambient temperature, relative humidity (RH), or age of the sensor in sampling days could be used in a correction algorithm to improve the agreement. Maximum improvement in agreement with a reference, incorporating all factors, was observed for an NO2 sensor (multiple correlation coefficient R2adj-orig = 0.57, R2adj-final = 0.81); however, other sensors showed no apparent improvement in agreement. A four-node sensor network was successfully able to capture ozone (2 nodes) and PM (4 nodes) data for an 8 month period of time and show expected diurnal concentration patterns, as well as potential ozone titration due to near-by traffic emissions. Overall, this study demonstrates a straightforward methodology for establishing low-cost air quality sensor performance in a real-world setting and demonstrates the feasibility of deploying a local sensor network to measure ambient air quality trends.

A Comparison of Immersion Gold and Tin Surface Finishes on Sensing Electrodes for PCB Environmental Saltwater Concentration Sensors

2016 ◽  
Vol 2016 (1) ◽  
pp. 000557-000562
Author(s):  
Robert N. Dean ◽  
Frank T. Werner ◽  
Michael J. Bozack
Keyword(s):  
Surface Finish ◽  
Printed Circuit Board ◽  
Chemical Constituents ◽  
Low Cost ◽  
Circuit Board ◽  
Testing Environment ◽  
Printed Circuit ◽  
Sensor Performance ◽  
Sensing Applications ◽  
Surface Finishes

Abstract Printed circuit board (PCB) sensors using low-cost commercial printed circuit board fabrication processes have been demonstrated for environmental sensing applications. One configuration of these sensors uses exposed electrodes to measure saltwater concentration in freshwater/seawater mixtures, through monitoring the resistance between the electrodes when they are immersed in the saltwater/freshwater solution. The lowest cost commercial PCB processes use an immersion Sn HASL surface finish on exposed copper cladding, including the sensing electrodes. This commercial PCB process has been demonstrated to make an effective, low-cost, short-lifetime sensor for saltwater concentration testing. The Sn finish, however, may not be optimal for this application. Sn oxidizes, which can interfere with sensor performance. Additionally, Sn and Sn oxides are potentially reactive with chemical constituents in seawater and seawater/freshwater solutions. An immersion Au (ENIG) surface finish is certainly less reactive with the atmosphere and chemicals likely present in the testing environment. However, an immersion Au finish increases the cost of the sensors by 30% to 40%. To investigate if the possible benefits of the more expensive Au surface finish are worth the extra expense, a study was performed where identical PCB sensors were procured from a commercial vendor with their standard low-cost Sn HASL finish and with their standard ENIG surface finish. Both sets of sensors were then evaluated in concentrations of seawater and freshwater, from 0% to 100% seawater concentration, using freshwater samples from a natural freshwater source near the coast where the seawater was obtained. Testing demonstrated an insignificant difference in sensor performance between the Sn HASL and the ENIG coated sensing electrodes. The results of this investigation indicated that for applications where the sensors will not be used for long periods of time, the added expense of an immersion Au surface finish is not worth the added cost.

Parallel Robot Calibration Utilizing Adaptronic Joints

2008 ◽  
Author(s):  
Philipp Last ◽  
Annika Raatz ◽  
Ju¨rgen Hesselbach ◽  
Nenad Pavlovic ◽  
Ralf Keimer
Keyword(s):  
Parallel Robot ◽  
Parallel Robots ◽  
Machine Component ◽  
Robot Calibration ◽  
Geometric Calibration ◽  
Self Calibration ◽  
Mechanical Constraints ◽  
Special Machine ◽  
Calibration Methods ◽  
Calibration Techniques

Model based geometric calibration is well known to be an efficient way to enhance absolute accuracy of robotic systems. Generally its application requires redundant measurements, which are achieved by external metrology equipment in most traditional calibration techniques. However, these methods are usually time-consuming, expensive and inconvenient. Thus, so-called self-calibration methods have achieved attention from researchers, which either use internal sensors or rely on mechanical constraints instead. In this paper a new self-calibration technique is presented for parallel robots which is motivated by the idea of constrained calibration. The new approach utilizes a special machine component called the adaptronic swivel joint in order to achieve the required redundant information. Compared to similar approaches it offers several advantages. The new calibration scheme is described and verified in simulation studies using a RRRRR-structure as an example.

scholarly journals Towards low-cost and high-performance air pollution measurements using machine learning calibration techniques

2020 ◽  
Author(s):  
Peer Nowack ◽  
Lev Konstantinovskiy ◽  
Hannah Gardiner ◽  
John Cant
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Ridge Regression ◽  
Low Cost ◽  
Reference Signal ◽  
Machine Learning Algorithms ◽  
Public Health Issue ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Calibration Techniques

Abstract. Air pollution is a key public health issue in urban areas worldwide. The development of low-cost air pollution sensors is consequently a major research priority. However, low-cost sensors often fail to attain sufficient measurement performance compared to state-of-the-art measurement stations, and typically require calibration procedures in expensive laboratory settings. As a result, there has been much debate about calibration techniques that could make their performance more reliable, while also developing calibration procedures that can be carried out without access to advanced laboratories. One repeatedly proposed strategy is low-cost sensor calibration through co-location with public measurement stations. The idea is that, using a regression function, the low-cost sensor signals can be calibrated against the station reference signal, to be then deployed separately with performances similar to the original stations. Here we test the idea of using machine learning algorithms for such regression tasks using hourly-averaged co-location data for nitrogen dioxide (NO2) and particulate matter of particle sizes smaller than 10 μm (PM10) at three different locations in the urban area of London, UK. Specifically, we compare the performance of Ridge regression, a linear statistical learning algorithm, to two non-linear algorithms in the form of Random Forest (RF) regression and Gaussian Process regression (GPR). We further benchmark the performance of all three machine learning methods to the more common Multiple Linear Regression (MLR). We obtain very good out-of-sample R2-scores (coefficient of determination) > 0.7, frequently exceeding 0.8, for the machine learning calibrated low-cost sensors. In contrast, the performance of MLR is more dependent on random variations in the sensor hardware and co-located signals, and is also more sensitive to the length of the co-location period. We find that, subject to certain conditions, GPR is typically the best performing method in our calibration setting, followed by Ridge regression and RF regression. However, we also highlight several key limitations of the machine learning methods, which will be crucial to consider in any co-location calibration. In particular, none of the methods is able to extrapolate to pollution levels well outside those encountered at training stage. Ultimately, this is one of the key limiting factors when sensors are deployed away from the co-location site itself. Consequently, we find that the linear Ridge method, which best mitigates such extrapolation effects, is typically performing as good as, or even better, than GPR after sensor re-location. Overall, our results highlight the potential of co-location methods paired with machine learning calibration techniques to reduce costs of air pollution measurements, subject to careful consideration of the co-location training conditions, the choice of calibration variables, and the features of the calibration algorithm.

scholarly journals A galvanic-based dissolved oxygen level monitoring sensor system in freshwater ponds

2021 ◽  
Vol 9 (2) ◽  
pp. 83-89
Author(s):  
Damar Wicaksono ◽  
Tatag Lindu Bhakti ◽  
Restiadi Bayu Taruno ◽  
Melvin Rahma Sayuga Subroto ◽  
Anita Mustikasari
Keyword(s):  
Dissolved Oxygen ◽  
Low Cost ◽  
Reference Electrode ◽  
Water Quality Monitoring ◽  
Input Voltage ◽  
Step Response ◽  
Active Electrode ◽  
Sensor Performance ◽  
Sensor Structure ◽  
Reference Layer

This study aims to develop low-cost and environmentally friendly material galvanic-based dissolved oxygen sensors. A Dissolved oxygen (DO) sensor has been designed and fabricated on an 85 x 205 mm galvanic-based. The sensor structure device consists of Al-Zn reference layer electrode, Ag/AgCl active electrode, 120ml KCl electrolyte solvent 0,1 M, and closed by TiO2 membrane (PTFE). The Al-Zn formation reference electrode was done by Ag layer chlorination using FeCl3, and the TiO2 membrane was formed by TiO2 paste screen printing. The test was done to measure the sensor’s performance based on the current-voltage characteristics between 1.0 and 1.8 V. The results showed that a stable diffusion current was obtained when the input voltage was 1.5 V, resulting in the best sensor performance with a sensitivity of 0.7866 μA L/mg and a stable step response time of 3 mins. This prototype sensor showed high potential for prototyping for a low-cost water quality monitoring system.

Sensor Performance Assessment Based on a Physical Model and Impedance Measurements

2013 ◽  
Vol 569-570 ◽  
pp. 751-758 ◽  
Author(s):  
Inka Buethe ◽  
Claus Peter Fritzen
Keyword(s):  
Low Cost ◽  
Embedded Sensors ◽  
Strain Gauges ◽  
Bonding Layer ◽  
Temperature Dependent ◽  
Active Sensors ◽  
Operational Conditions ◽  
Sensor Performance ◽  
Piezoelectric Wafer Active Sensors ◽  
Piezoelectric Wafer

The employment of a large number of embedded sensors in advanced monitoring systems becomes more common, enabling in-service detection, localization and assessment of defects in mechanical, civil and aerospace structures. These sensors could be optical fibre sensors, accelerometers, strain gauges or piezoelectric wafer active sensors (PWAS). As the latter are quite popular, due to its multipurpose application as actuators and sensors and its low cost, this type will be investigated. Within this paper a possible approach of sensor performance is presented. The method uses the coupled electro-mechanical admittance to detect damage of the PWAS and its bonding layer. The help of a temperature dependent theoretical model provides for influences of changing environmental and operational conditions. The model will be compared with FEM-results, before showing the successful application on experimental results.

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