The aim of the work is to develop an automated measuring system in a mechanical gyrocompass with the help of specially developed hardware and software in order to facilitate the operation of the device and minimize observer errors. The developed complex provides automation only for the time method, as for the method of the turning point it is necessary to constantly contact the motion screw of the total station. The project is based on an integrated system, the hardware part of which contains a single-board computer, camera, and lens. The main software is a developed motion recognition algorithm with the help of image processing. This algorithm was created using the Python programming language and the open-source computer vision library OpenCV. With the help of the hardware, a video image of the gyroscope's reference scale is obtained, and with the help of the software, the moving light indicator and its position relative to the scale are identified in this image. The result of the study is a functioning automatic measurement system, which determines the value of the azimuth of the direction with the same accuracy as manual measurements. The system is controlled remotely via a computer and wi-fi network. To test the system, a series of automatic and manual measurements were performed simultaneously at the same point for the same direction. Based on the results obtained, it can be stated that the accuracy of the system is within the limits specified by the manufacturer of the device for manual measurements. The application of computer vision technology, namely the tracking of a moving object in the image for gyroscopic measurements can give a significant impetus to the development of automation systems for a wide range of measuring instruments, which in turn can improve the accuracy of measurement results. The developed system can be used together with the Gyromax AK-2M gyrocompass of GeoMessTechnik for carrying out automated measurements, training of new operators. With the help of the developed model, it is possible to avoid gross errors of the observer, to facilitate the measurement process which will not demand the constant presence of the operator near the device. In some dangerous conditions, this is a significant advantage.
1. Albert, S., & Surducan, V. (2017, December). Raspberry Pi camera with intervalometer used as crescograph. In AIP Conference Proceedings (Vol. 1917, No. 1, p. 030009). AIP Publishing LLC. https://doi.org/10.1063/1.5018282
2. Barbour, N., & Schmidt, G. (2001). Inertial sensor technology trends. IEEE Sensors Journal, 1(4), 332-339. doi:10.1109/7361.983473. https://doi.org/10.1109/7361.983473
3. Cuciuc, M. (2018). Suitability of the Raspberry Pi camera for cosmic ray detection and measurement. 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC). https://doi.org/10.1109/NSSMIC.2018.8824441
4. Dinesh, M., & Bhaskar, K. B. (2020). Smart Highway Accident Alert Using Raspberry Pi Camera. Journal of Digital Integrated Circuits in Electrical Devices. Volume-5, Issue-1 (January-April, 2020)
5. Gollapudi, S. (2019). OpenCV with Python. Learn Computer Vision Using OpenCV, 31-50. doi:10.1007/978-1-4842-4261-2_2. https://doi.org/10.1007/978-1-4842-4261-2_2
6. Heger, W., Trevoho, I., & Lopatin, Y. (2019). Investigations to digitizing of the gyro oscillation swing by a line camera. Modern achievements of geodetic science and industry. (2), 45-53. (in Ukrainian). https://doi.org/10.33841/1819-1339-2019-2-38-45-53
7. Heister, H., & Liebl, W. (2016). Measurement Uncertainty of Gyro-measurements in the Construction Works of the Gotthard Base Tunnel. The surveyors in the longest tunnel of the world. Special Edition English. Ingenieur-Geometer Schweiz (IGS).
8. Johnson, B. R., Cabuz, E., French, H. B., & Supino, R. (2010). Development of a MEMS gyroscope for northfinding applications. IEEE/ION Position, Location and Navigation Symposium. https://doi.org/10.1109/PLANS.2010.5507133
9. Juang, J., & Radharamanan, R. (2009). Evaluation of Ring Laser and Fiber Optic Gyroscope Technology. Proceedings of the American Society for Engineering Education, Middle Atlantic Section ASEE Mid-Atlantic Fall 2009 Conference
King of Prussia, PA, USA. 23-24 October 2009
10. Parent, A., Le Traon, O., Masson, S., & Le Foulgoc, B. (2007). A Coriolis Vibrating Gyro Made of a Strong Piezoelectric Material. 2007 IEEE Sensors. https://doi.org/10.1109/ICSENS.2007.4388541
11. Sun, H., Zhang, F., & Li, H. (2010, August). Design and implementation of fiber optic gyro north-seeker. In 2010 IEEE International Conference on Mecha¬tronics and Automation (pp. 1058-1062). IEEE. https://doi.org/10.1109/ICMA.2010.5589722
12. Wetherelt, A., & Hunt, P. (2004). Azimuth Determinations Using an Adapted Wild GAK1. Survey Review 37(294):592-603, October 2004. https://doi.org/10.1179/sre.2004.37.294.592
13. Zhen, S., Zhiqiang, Y., & Zhe, Z. (2013). Study on Automatic North-Seeking Key Technologies of Maglev Gyroscope. The Open Mechanical Engineering Journal, 2013, 7, 83-89. https://doi.org/10.2174/1874155X01307010083
1. Albert, S., & Surducan, V. (2017, December). Raspberry Pi camera with intervalometer used as crescograph. In AIP Conference Proceedings (Vol. 1917, No. 1, p. 030009). AIP Publishing LLC. https://doi.org/10.1063/1.5018282
2. Barbour, N., & Schmidt, G. (2001). Inertial sensor technology trends. IEEE Sensors Journal, 1(4), 332-339. doi:10.1109/7361.983473. https://doi.org/10.1109/7361.983473
3. Cuciuc, M. (2018). Suitability of the Raspberry Pi camera for cosmic ray detection and measurement. 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC). https://doi.org/10.1109/NSSMIC.2018.8824441
4. Dinesh, M., & Bhaskar, K. B. (2020). Smart Highway Accident Alert Using Raspberry Pi Camera. Journal of Digital Integrated Circuits in Electrical Devices. Volume-5, Issue-1 (January-April, 2020)
5. Gollapudi, S. (2019). OpenCV with Python. Learn Computer Vision Using OpenCV, 31-50. doi:10.1007/978-1-4842-4261-2_2. https://doi.org/10.1007/978-1-4842-4261-2_2
6. Heger, W., Trevoho, I., & Lopatin, Y. (2019). Investigations to digitizing of the gyro oscillation swing by a line camera. Modern achievements of geodetic science and industry. (2), 45-53. (in Ukrainian). https://doi.org/10.33841/1819-1339-2019-2-38-45-53
7. Heister, H., & Liebl, W. (2016). Measurement Uncertainty of Gyro-measurements in the Construction Works of the Gotthard Base Tunnel. The surveyors in the longest tunnel of the world. Special Edition English. Ingenieur-Geometer Schweiz (IGS).
8. Johnson, B. R., Cabuz, E., French, H. B., & Supino, R. (2010). Development of a MEMS gyroscope for northfinding applications. IEEE/ION Position, Location and Navigation Symposium. https://doi.org/10.1109/PLANS.2010.5507133
9. Juang, J., & Radharamanan, R. (2009). Evaluation of Ring Laser and Fiber Optic Gyroscope Technology. Proceedings of the American Society for Engineering Education, Middle Atlantic Section ASEE Mid-Atlantic Fall 2009 Conference
King of Prussia, PA, USA. 23-24 October 2009
10. Parent, A., Le Traon, O., Masson, S., & Le Foulgoc, B. (2007). A Coriolis Vibrating Gyro Made of a Strong Piezoelectric Material. 2007 IEEE Sensors. https://doi.org/10.1109/ICSENS.2007.4388541
11. Sun, H., Zhang, F., & Li, H. (2010, August). Design and implementation of fiber optic gyro north-seeker. In 2010 IEEE International Conference on Mecha¬tronics and Automation (pp. 1058-1062). IEEE. https://doi.org/10.1109/ICMA.2010.5589722
12. Wetherelt, A., & Hunt, P. (2004). Azimuth Determinations Using an Adapted Wild GAK1. Survey Review 37(294):592-603, October 2004. https://doi.org/10.1179/sre.2004.37.294.592
13. Zhen, S., Zhiqiang, Y., & Zhe, Z. (2013). Study on Automatic North-Seeking Key Technologies of Maglev Gyroscope. The Open Mechanical Engineering Journal, 2013, 7, 83-89. https://doi.org/10.2174/1874155X01307010083