Оптимизация навигационных датчиков МЭМС с применением искусственного интеллекта для улучшения пользовательского опыта

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Кусственного интеллекта (ИИ), применяемого к навигационным датчикам микроэлектромеханических систем (МЭМС). Основная цель - улучшение пользовательского опыта. Используя комплексный подход, исследуются методы, основанные на искусственном интеллекте, включающие слияние датчиков, адаптивную фильтрацию, калибровку, компенсацию, прогнозное моделирование и энергоэффективность. Через строгое проведение кейс-исследований и использование эмпирических данных данное исследование подтверждает значительные достижения, включая повышенную точность, снижение энергопотребления, увеличение надежности и усиление удовлетворенности пользователя, в различных приложениях, таких как автономные транспортные средства, внутреннее определение положения, носимые устройства и беспилотные системы. В заключении данное исследование подчеркивает трансформационный потенциал оптимизации на основе ИИ в навигационных датчиках МЭМС, признавая при этом наличие постоянных вызовов, таких как вычислительная сложность, доступность данных и обработка в реальном времени проведения дальнейших исследований, ориентированных на инновационные методологии ИИ, их интеграцию с передовыми технологиями с условием соблюдения принципов дизайна, ориентированных на человека, и установление строгих стандартов оценки. Подобные исследования позволят использовать весь потенциал механизмов оптимизации на основе методов ИИ, внедряя передовые и ориентированные на пользователя навигационные системы и в конечном итоге повышая уровень удобства пользователей в различных областях применения подобных систем.

Об авторах

Али Ализадех

Технологический университет имени К.Н. Туси

Автор, ответственный за переписку.
Email: ali.rim.alizadeh@gmail.com
ORCID iD: 0009-0006-0673-1893

магистрант департамента механики и процессов управления, инженерная академия, Российский университет дружбы народов; магистрант кафедры космической инженерии, факультет аэрокосмической техники, Технологический университет К.Н. Туси

Москва, Российская Федерация; Тегеран, Иран

Ольга Александровна Салтыкова

Российский университет дружбы народов

Email: saltykova-oa@rudn.ru
ORCID iD: 0000-0002-3880-6662

кандидат физико-математических наук, доцент департамента механики и процессов управления, инженерная академия

Москва, Российская Федерация

Алиреза Басохбат Новинзадех

Технологический университет имени К.Н. Туси

Email: novinzadeh@kntu.ac.ir
ORCID iD: 0000-0002-8123-6968

доктор наук в области космической инженерии, доцент, заведующий кафедрой космической инженерии, факультет аэрокосмической инженерии

Тегеран, Иран

Список литературы

  1. Le MQ, Capsal JF, Lallart M, Hebrard Y, Ham AVD, Reffe N, Geynet L, Cottinet P-J. Review on energy harvesting for structural health monitoring in aeronautical applications. Progress in Aerospace Sciences. 2015;79:147-157. https://doi.org/10.1016/j.paerosci.2015.10.001
  2. Kraft M, White Neil M. MEMS for Automotive and Aerospace Applications. Woodhead Publishing Limited; 2013. eBook ISBN: 9780857096487
  3. Marope T. Future Technological Factors Affecting Unmanned Aircraft Systems (UAS): A South African Perspective Towards 2025. PhD. Dissertation, Nelson Mandela Metropolitan University, 2014.
  4. Benser ET, Shkel AM. Trends in Inertial Sensors and Applications. 2015 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL) Proceedings, Hapuna Beach, HI, USA, 2015. https://doi.org/10.1109/ISISS.2015.7102358
  5. Al Bitar N, Gavrilov A, Khalaf W. Artificial Intelligence Based Methods for Accuracy Improvement of Integrated Navigation Systems During GNSS Signal Outages: An Analytical Overview. Gyroscopy Navigation. 2020;11(1):41-58. https://doi.org/10.1134/S2075 108720010022
  6. Mahdi AE, Azouz A, Abdalla AE, Abosekeen A. A Machine Learning Approach for an Improved Inertial Navigation System Solution. Sensors. 2022;22(4). https://doi.org/10.3390/s22041687
  7. Wang C, Cui Y, Liu Y, Li K, Shen C. High-G MEMS Accelerometer Calibration Denoising Method Based on EMD and Time-Frequency Peak Filtering. Micromachines (Basel). 2023;14(5):970. https://doi.org/ 10.3390/mi14050970
  8. Noureldin A, El-Shafie A, Reda Taha M. Optimizing neuro-fuzzy modules for data fusion of vehicular navigation systems using temporal cross-validation. Engineering Applications of Artificial Intelligence. 2007;20(1):49-61. https://doi.org/10.1016/j.engappai.2006.03.002
  9. Biswas A, Wang HC. Autonomous Vehicles Enabled by the Integration of IoT, Edge Intelligence, 5G, and Blockchain. Sensors. 2023;23(4). https://doi.org/10.3390/s23041963
  10. Li Duan F. When AIAA Meets IEEE Intelligent Aero-Engine and Electric Aircraft. Springer; 2023. https://doi.org/10.1007/978-981-19-8394-8
  11. Zhuang Y, Sun X, Li Y et al. Multi-sensor integrated navigation/positioning systems using data fusion: From analytics-based to learning-based approaches. Information Fusion. 2023;95:62-90. https://doi.org/10.1016/j.inffus.2023.01.025
  12. Quinchia AG, Falco G, Falletti E, Dovis F, Ferrer C. A comparison between different error modeling of MEMS applied to GPS/INS integrated systems. Sensors. 2013;13(8):9549-9588. https://doi.org/10.3390/s130809549
  13. Nabavi S, Zhang L. MEMS Piezoelectric Energy Harvester Design and Optimization Based on Genetic Algorithm; 2016 IEEE International Ultrasonics Symposium (IUS): 18-21 Sept. 2016. IEEE International Ultrasonics Symposium Proceedings. Published online 2016. https://doi.org/10.1109/ULTSYM.2016.7728786
  14. Xi F, Pang Y, Liu G et al. Self-powered intelligent buoy system by water wave energy for sustainable and autonomous wireless sensing and data transmission. Nano Energy. 2019;61:1-9. https://doi.org/10.1016/j.nanoen.2019.04.026
  15. Hajare R, Reddy V, Srikanth R. MEMS based sensors - A comprehensive review of commonly used fabrication techniques. In: Elsevier-Materials Today: Proceedings. Elsevier Ltd; 2021;49:720-730. https://doi.org/10.1016/j.matpr.2021.05.223
  16. Shi LF, Zhao Y Le, Liu GX, Chen S, Wang Y, Shi YF. A Robust Pedestrian Dead Reckoning System Using Low-Cost Magnetic and Inertial Sensors. IEEE Trans Instrum Meas. 2019;68(8):2996-3003. https://doi.org/10.1109/TIM.2018.2869262
  17. Goebel R, Tanaka Y, Wahlster W. Autonomous and Intelligent Systems. Vol. 283. (Kamel M, Karray F, Hagras H, eds.). Springer Berlin Heidelberg; 2012. https://doi.org/10.1007/978-3-642-31368-4
  18. Fontanella R, Accardo D, Caricati E, Cimmino S, Simone D De. An Extensive Analysis for the Use of Back Propagation Neural Networks to Perform the Calibration of MEMS Gyro Bias Thermal Drift. IEEE. Published online 2016:1-9.
  19. Chimeh HE, Nabavi S, Janaideh MAl, Zhang L. Deep-Learning-Based Optimization for a Low-Frequency Piezoelectric MEMS Energy Harvester. IEEE Sens J. 2021;21(19):21330-21341. https://doi.org/10.1109/JSEN.2021.3102537
  20. Fitzgerald AM, Fitzgerald AM. MEMS Inertial Sensors. In: Morton YTJ, Frank van Diggelen, Spilker JJ, Parkinson BW, eds. Position, Navigation, and Timing Technologies in the 21st Century: Integrated Satellite Navigation, Sensor Systems, and Civil Applications. Vol. 2. 1st ed. John Wiley & Sons; 2021:1435-1446.
  21. Park M. Error Analysis and Stochastic Modeling of MEMS Based Inertial Sensors for Land Vehicle Navigation Applications. Master thesis. University of Calgary; 2004.
  22. García JA, Lara E, Aguilar L. A low-cost calibration method for low-cost MEMS accelerometers based on 3D printing. MDPI-Sensors. 2020;20(22):1-19. м10.3390/s20226454
  23. Han S, Meng Z, Omisore O, Akinyemi T, Yan Y. Random error reduction algorithms for MEMS inertial sensor accuracy improvement - a review. Micromachines (Basel). 2020;11(11):1-36. https://doi.org/10.3390/mi11111021
  24. Fontanella R, Accardo D, Lo Moriello RS, Angrisani L, De Simone D. MEMS gyros temperature calibration through artificial neural networks. Sens Actuators A Phys. 2018;279:553-565. https://doi.org/ 10.1016/j.sna.2018.04.008
  25. Hua Y, Wang S, Li B, Bai G, Zhang P. Dynamic modeling and anti-disturbing control of an electromagnetic mems torsional micromirror considering external vibrations in vehicular lidar. Micromachines (Basel). 2021;12(1):1-16. https://doi.org/10.3390/mi12010069
  26. Jamil F, Iqbal N, Ahmad S, Kim DH. Toward accurate position estimation using learning to prediction algorithm in indoor navigation. Sensors (Switzerland). 2020;20(16):1-27. https://doi.org/10.3390/s20164410
  27. Couchot JF, Deschinkel K, Salomon M. Active MEMS-based flow control using artificial neural network. Mechatronics. 2013;23(7):898-905. https:// doi.org/10.1016/j.mechatronics.2013.02.010
  28. Berndt JO, Petta P, Unland R. Proceedings Multiagent System Technologies. (Goebel R, Tanaka Y, Wahlster W, eds.). Springer; 2017. https://doi.org/10. 1007/978-3-319-64798-2
  29. Podder I, Fischl T, Bub U. Artificial Intelligence Applications for MEMS-Based Sensors and Manufacturing Process Optimization. Telecom. 2023; 4(1):165-197. м10.3390/telecom4010011 30. Algamili AS, Khir MHM, Dennis JO et al. A Review of Actuation and Sensing Mechanisms in MEMS-Based Sensor Devices. Nanoscale Research Letters. 2021;16(1). https://doi.org/10.1186/s11671-021-03481-7
  30. Shen C, Zhang Y, Guo X et al. Seamless GPS/Inertial Navigation System Based on Self-Learning Square-Root Cubature Kalman Filter. IEEE Transac- tions on Industrial Electronics. 2021;68(1):499-508. https://doi.org/10.1109/TIE.2020.2967671
  31. Scrivener M, Carmical P. Recommender System with Machine Learning and Artificial Intelligence. (Mohanty SN, Chatterjee JM, Jain S, Elngar AA, Gupta P, eds.). Wiley; 2020.
  32. Abdolkarimi ES, Abaei G, Mosavi MR. A wavelet-extreme learning machine for low-cost INS/ GPS navigation system in high-speed applications. GPS Solutions. 2018;22(1). https://doi.org/10.1007/s10291- 017-0682-x
  33. Chen F, Chang H, Yuan W, Wilcock R, Kraft M. Parameter optimization for a high-order band-pass continuous-time sigma-delta modulator MEMS gyroscope using a genetic algorithm approach. Journal of Micromechanics and Microengineering. 2012;22(10). https://doi.org/10.1088/0960-1317/22/10/105006
  34. Liang S, Zhu W, Zhao F, Wang C. Highefficiency wavelet compressive fusion for improving MEMS array performance. Sensors (Switzerland). 2020;20(6). https://doi.org/10.3390/s20061662
  35. Caruso D. Improving Visual-Inertial Navigation Using Stationary Environmental Magnetic Disturbances. Doctoral thesis. Paris-Saclay University; 2018. https://theses.hal.science/tel-01886847
  36. Dong J, Zhuang D, Huang Y, Fu J. Advances in multi-sensor data fusion: Algorithms and applications. Sensors. 2009;9(10):7771-7784. https://doi. org/10.3390/s91007771
  37. Mostafa MZ, Khater HA, Rizk MR, Bahasan AM. GPS/DVL/MEMS-INS smartphone sensors integrated method to enhance USV navigation system based on adaptive DSFCF. IET Radar, Sonar and Navigation. 2019;13(10):1616-1627. https://doi.org/10.1049/iet-rsn.2019.0015
  38. Ma H, Mu X, He B. Adaptive navigation algorithm with deep learning for autonomous underwater vehicle. Sensors. 2021;21(19). https://doi.org/ 10.3390/s21196406
  39. Xing H, Hou B, Lin Z, Guo M. Modeling and compensation of random drift of MEMS gyroscopes based on least squares support vector machine optimized by chaotic particle swarm optimization. Sensors (Switzerland). 2017;17(10). https://doi.org/10.3390/s17102335
  40. Huang L, Li H, Yu B et al. Combination of smartphone mems sensors and environmental prior information for pedestrian indoor positioning. Sensors (Switzerland). 2020;20(8). https://doi.org/10.3390/s200 82263
  41. Šegviü M, Krajþek K, Ivanjko E. Technologies for distributed flight control systems: A review. 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2015:1060-1065. https://doi.org/ 10.1109/MIPRO.2015.7160432
  42. Nevlydov I, Filipenko O, Volkova M, Ponomaryova G. MEMS-Based Inertial Sensor Signals and Machine Learning Methods for Classifying Robot Motion. In: 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). Lviv, Ukraine, 2018:13-16. https://doi.org/10.1109/DSMP.2018.8478613
  43. Fouché GJ, Malekian R. Drone as an autonomous aerial sensor system for motion planning. Measurement (Lond). 2018;119:142-155. https://doi.org/ 10.1016/j.measurement.2018.01.027
  44. Jiang C, Chen S, Chen Y et al. A MEMS IMU de-noising method using long short-term memory recurrent neural networks (LSTM-RNN). Sensors. 2018;18(10). https://doi.org/10.3390/s18103470
  45. Pokhrel N. Drone Obstacle Avoidance and Navigation to Using Artificial Intelligence. Master’s Thesis. Aalto University; 2018.
  46. Khan YA, Imaduddin S, Singh YP, Wajid M, Usman M, Abbas M. Artificial Intelligence Based Approach for Classification of Human Activities Using MEMS Sensors Data. Sensors. 2023;23(3):1275. https://doi.org/10.3390/s23031275
  47. Cao Z, Hu L, Yi G, Wang Z. Arm Motion Capture and Recognition Algorithm Based on MEMS Sensor Networks and KPA. IEEE. 2021:133-139. https://doi.org/10.1109/EIECS53707.2021.9588046
  48. Xing J, Wang X, Dong J. Big data tracking and automatic measurement technology for unmanned aerial vehicle trajectory based on MEMS sensor. Springer. 2022;26(9):4237-4247. https://doi.org/10.1007/s00500-021-06457-y
  49. Bieliakov R. Simulation of platform-free inertial navigation system of unmanned aerial vehicles based on neural network algorithms. Technology Audit and Production Reserves. 2021;1(2(57)):15-19. https://doi.org/10.15587/2706-5448.2021.225282
  50. Zahran S, Moussa A, El-Sheimy N, Sesay AB, Moussa A. Hybrid Machine Learning VDM for UAVs in GNSS-denied Environment. Journal of the Institute of Navigation. 2018;65(3):477-492. https://doi.org/10. 1002/navi.249
  51. Shi T, Wang H, Cui W, Ren L. Indoor space target searching based on EEG and EOG for UAV. Soft Computing. 2019;23(21):11199-11215. https://doi.org/10.1007/s00500-018-3670-3
  52. Borup KT, Fossen TI, Johansen TA. A machine learning approach for estimating air data parameters of small fixed-wing UAVs using distributed pressure sensors. IEEE Trans Aerosp Electron Syst. 2020;56(3): 2157-2173. https://doi.org/10.1109/TAES.2019.2945383
  53. Colombo C, Scala F, Trisolini M, Gonzalo Gòmez JL. The Environmental CubeSat Mission E.Cube for Low Earth Orbit Data Acquisition. 26th Conference of the Italian Association of Aeronautics and Astronautics (AIDAA 2021). 2021:1-6. https://hdl.handle.net/11311/1186118
  54. Cong L, Yue S, Qin H, Li B, Yao J. Implementation of a MEMS-Based GNSS/INS Integrated Scheme Using Supported Vector Machine for Land Vehicle Navigation. IEEE Sens J. 2020;20(23):14423-14435. https://doi.org/10.1109/JSEN.2020.3007892
  55. Silva MAC, Shan M, Cervone A, Gill E. Fuzzy control allocation of micro thrusters for space debris removal using CubeSats. Engineering Applications of Artificial Intelligence. 2019;81:145-156. https://doi.org/10.1016/j.engappai.2019.02.008
  56. Yue S, Cong L, Qin H, Li B, Yao J. A Robust Fusion Methodology for MEMS-Based Land Vehicle Navigation in GNSS-Challenged Environments. IEEE Access. 2020;8:44087-44099. https://doi.org/10.1109/ACCESS.2020.2977474
  57. Liddle JD, Holt AP, Jason SJ, O’Donnell KA, Stevens EJ. Space science with CubeSats and nanosatellites. Nat Astron. 2020;4(11):1026-1030. https://doi.org/10.1038/S41550-020-01247-2
  58. Smith MW, Donner A, Knapp M et al. On-orbit results and lessons learned from the ASTERIA space telescope mission. In Proc. 32nd Annual AIAA/USU Conference on Small Satellites SSC18-1-08. Logan, Utah, 2018.
  59. Dai H-F, Bian H-W, Wang R-Y, Ma H. An INS/ GNSS integrated navigation in GNSS denied environment using recurrent neural network. Defence Technology. 2020;16(2):334-340. https://doi.org/10.1016/j.dt.2019.08.011
  60. Nader Al Bitar, Gavrilov A, Khalaf W. Artificial Intelligence Based Methods for Accuracy Improvement of Integrated Navigation Systems During GNSS Signal Outages: An Analytical Overview. Gyroscopy and Navigation. 2020;11(1):41-58. https://doi.org/10.1134/S2075108720010022
  61. Du S, Zhang S, Gan X. A Hybrid Fusion Strategy for the Land Vehicle Navigation Using MEMS INS, Odometer and GNSS. IEEE Access. 2020;8:152512-152522. https://doi.org/10.1109/ACCESS.2020.3016004
  62. Mahdi AE, Azouz A, Abdalla AE, Abosekeen A. A Machine Learning Approach for an Improved Inertial Navigation System Solution. MDPI Sensors. 2022; 22(4). https://doi.org/10.3390/s22041687
  63. Shit RC. Precise localization for achieving next-generation autonomous navigation: State-of-theart, taxonomy and future prospects. Computer Communications. 2020;160:351-374. https://doi.org/10.1016/j.comcom.2020.06.007
  64. Esashi M. MEMS development focusing on collaboration using common facilities: a retrospective view and future directions. Microsyst Nanoeng. 2021; 7(1). https://doi.org/10.1038/s41378-021-00290-x

© Ализадех А., Салтыкова О.А., Новинзадех А.Б., 2023

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