Otonom Robotlarda IMU Verilerini Kullanan Karar Ağacı Tabanlı Yön Tespiti

Yıl 2024, Cilt: 14 Sayı: 1, 57 - 68, 07.07.2024

Nafiye Nur Apaydın , İrfan Kılıç , Muhammet Apaydın , Orhan Yaman

https://doi.org/10.55024/buyasambid.1501521

Öz

Konum tespiti, birçok uygulama alanında önemli bir role sahiptir. Bu çalışmada, yerel konumlandırma sistemi (GPS) ile yön tespiti yapmak için atalet ölçü birimi (IMU) verilerinin kullanıldığı bir makine öğrenmesi (ML) yöntemi geliştirilmesi amaçlanmıştır. Bu çalışma kapsamında, Arduino Mega, Altimu-10 IMU sensör, GPS modülü ve SD kart modülü kullanılarak bir elektronik kart tasarlanmıştır. Bu elektronik kart, bir otomobil üzerine yerleştirerek yeni bir veri seti oluşturulmuştur. Bu veri seti 1952x11 veriden oluşmaktadır. Bu veri seti ivmeölçer (x, y, z), jiroskop (x, y, z), pusula (x, y, z) ve GPS sensöründen alınan veriler yardımıyla elde edilmiştir. Bu çalışmada yön tespiti için Karar Ağacı Algoritması önerilmiştir. Elde edilen verilerden Enlem ve Boylam değerleriyle her konumun bir önceki konum ile açısı hesaplanmıştır. Daha sonra belirli bir açı aralığına göre Kuzey, Doğu, Güney ve Batı olmak üzere veriler 4 sınıfa ayrılmıştır. En sonunda da IMU verileri önerilen yöntemde kullanılarak yön tespit modeli geliştirilmiş ve yaklaşık %82,11 doğruluk(accuracy) elde edilmiştir.

Anahtar Kelimeler

Yön Tespiti, Karar Ağacı Algoritması, IMU, GPS Verileri

Destekleyen Kurum

TÜBİTAK

Proje Numarası

1919B012310014

Teşekkür

Bu çalışma “1919B012310014” numaralı TÜBİTAK 2209/A projesi ile desteklenmiştir.

Decision Tree-Based Direction Detection Using IMU Data in Autonomous Robots

Öz

Location detection plays a crucial role in various applications. In this study, a machine learning (ML) method using inertial measurement unit (IMU) data was developed to determine direction with the Global Positioning System (GPS). In this study, an electronic board was designed using an Arduino Mega, Altimu-10 IMU sensor, GPS module, and SD card module. This electronic board was placed on a car to create a new dataset. This dataset consists of 1952x11 data. The dataset was obtained using accelerometer (x, y, z), gyroscope (x, y, z), compass (x, y, z), and GPS sensor data. The Decision Tree Algorithm was proposed for direction determination in this study. The angles between each position and the previous position were calculated using the latitude and longitude values obtained from the collected data. Then, the data were divided into 4 classes: North, East, South, and West, based on specific angle ranges. Finally, a direction detection model was developed using IMU data in the proposed method, achieving an accuracy of approximately 82.11%.

Anahtar Kelimeler

Direction Detection, Decision Tree Algorithm, Inertial Measurement Unit (IMU), GPS Data

Proje Numarası

1919B012310014

Kaynakça

• Álvarez López, Yuri, María Elena de Cos Gómez, and Fernando Las-Heras Andrés. 2017. “A Received Signal Strength RFID-Based Indoor Location System.” Sensors and Actuators, A: Physical. doi: 10.1016/j.sna.2017.01.007.
• Ata, Emre Han. 2022. “Inertial-Navıgation-System Aiding by Combining Data Link and Seeker Measurements.” Middle East Technical University.
• Baygin, Nursena, Mehmet Baygin, and Mehmet Karakose. 2019. “A SVM-PSO Classifier for Robot Motion in Environment with Obstacles.” in 2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019.
• Chen, Changhao, Xiaoxuan Lu, Andrew Markham, and Niki Trigoni. 2018. “IoNet: Learning to Cure the Curse of Drift in Inertial Odometry.” in 32nd AAAI Conference on Artificial Intelligence, AAAI 2018.
• Deng, Zhenyun, Xiaoshu Zhu, Debo Cheng, Ming Zong, and Shichao Zhang. 2016. “Efficient KNN Classification Algorithm for Big Data.” Neurocomputing. doi: 10.1016/j.neucom.2015.08.112.
• Du, Hao, Wei Wang, Chaowen Xu, Ran Xiao, and Changyin Sun. 2020. “Real-Time Onboard 3D State Estimation of an Unmanned Aerial Vehicle in Multi-Environments Using Multi-Sensor Data Fusion.” Sensors (Switzerland). doi: 10.3390/s20030919.
• Farooq, Asad, and Shaharyar Kamal. 2019. “Indoor Positioning and Tracking Using Sensors of a Smart Device.” in 2019 International Conference on Applied and Engineering Mathematics, ICAEM 2019 - Proceedings.
• Girgensohn, Andreas, Mitesh Patel, and Jacob T. Biehl. 2024. “Radio-Frequency-Based Indoor-Localization Techniques for Enhancing Internet-of-Things Applications.” Personal and Ubiquitous Computing. doi: 10.1007/s00779-020-01446-8.
• Gögüş, Bekir. 2022. “Ataletsel Navigasyon Sistemlerinde Kestirim Için Farklı Yöntemlerin Performanslarının Karşılaştırılması.” Başkent Üniversitesi.
• Greff, Klaus, Rupesh K. Srivastava, Jan Koutnik, Bas R. Steunebrink, and Jurgen Schmidhuber. 2017. “LSTM: A Search Space Odyssey.” IEEE Transactions on Neural Networks and Learning Systems. doi: 10.1109/TNNLS.2016.2582924.
• Hernández Sánchez, Sara, Rubén Fernández Pozo, and Luis Alfonso Hernández Gómez. 2019. “Deep Neural Networks for Driver Identification Using Accelerometer Signals from Smartphones.” in Lecture Notes in Business Information Processing.
• Iv, James H. Keppe., Brian C. Claus, and James C. Kinsey. 2019. “A Navigation Solution Using a MEMS IMU, Model-Based Dead-Reckoning, and One-Way-Travel-Time Acoustic Range Measurements for Autonomous Underwater Vehicles.” IEEE Journal of Oceanic Engineering. doi: 10.1109/JOE.2018.2832878.
• Karabey, Işıl. 2015. “Wi-Fi Tabanlı Parmak Izi Yöntemi Kullanarak Iç Ortam Konumlandırma.” Atatürk Üniversitesi.
• Kaya, Sertaç Buğra. 2018. “Çoklu Veri Füzyonu Tabanlı İç Ortam Konumlandırma ve Takip Sistemi Tasarımı.” Hacettepe Üniversitesi.
• Khanh, Tran Trong, Van Dung Nguyen, Xuan Qui Pham, and Eui Nam Huh. 2020. “Wi-Fi Indoor Positioning and Navigation: A Cloudlet-Based Cloud Computing Approach.” Human-Centric Computing and Information Sciences. doi: 10.1186/s13673-020-00236-8.
• Kopar, Ahmet Serdar. 2020. “İnsansız Hava Araçlarının (İHA) Konumlandırılmasında Kullanılan Sensör Verilerinin Filtre Teknikleri Kullanılarak Iyileştirilmesi.” Erzurum Teknik Üniversitesi.
• Li, Yi Shan, and Fang Shii Ning. 2018. “Low-Cost Indoor Positioning Application Based on Map Assistance and Mobile Phone Sensors.” Sensors (Switzerland). doi: 10.3390/s18124285.
• Li, Yunhui, Shize Yang, Xianchao Xiu, and Zhonghua Miao. 2022. “A Spatiotemporal Calibration Algorithm for IMU–LiDAR Navigation System Based on Similarity of Motion Trajectories.” Sensors. doi: 10.3390/s22197637.
• Mahdi, Ahmed E., Ahmed Azouz, Ahmed E. Abdalla, and Ashraf Abosekeen. 2022. “A Machine Learning Approach for an Improved Inertial Navigation System Solution.” Sensors. doi: 10.3390/s22041687.
• Naviani, Avinash. 2018. “KNN Classification Tutorial Using Scikit-Learn.” Datacamp.
• Oguntala, George, Raed Abd-Alhameed, Stephen Jones, James Noras, Mohammad Patwary, and Jonathan Rodriguez. 2018. “Indoor Location Identification Technologies for Real-Time IoT-Based Applications: An Inclusive Survey.” Computer Science Review.
• Okudan, Mehmet Emin. 2019. “Navigasyon Hassasiyetini Arttırmak Için Ataletsel Ölçüm Birimine Tamamlayıcı Filtre Uygulanması.” İstanbul Teknik Üniversitesi.
• Poulose, Alwin, Odongo Steven Eyobu, and Dong Seog Han. 2019. “An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data.” IEEE Access. doi: 10.1109/ACCESS.2019.2891942.
• Poulose, Alwin, and Dong Seog Han. 2019. “Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera.” Sensors (Switzerland). doi: 10.3390/s19235084.
• Prikhodko, Igor P., Brock Bearss, Carey Merritt, Joe Bergeron, and Charles Blackmer. 2018. “Towards Self-Navigating Cars Using MEMS IMU: Challenges and Opportunities.” in 5th IEEE International Symposium on Inertial Sensors and Systems, INERTIAL 2018 - Proceedings.
• Rajesh, B., M. Vishnu Sai Vardhan, and L. Sujihelen. 2020. “Leaf Disease Detection and Classification by Decision Tree.” in Proceedings of the 4th International Conference on Trends in Electronics and Informatics, ICOEI 2020.
• Regus, M., R. Talar, and R. Labudzki. 2019. “Indoor Positioning and Navigation System for Autonomous Vehicles Based on RFID Technology.” in IOP Conference Series: Materials Science and Engineering.
• Şahin, Fatih, and Faruk Ulamış. 2023. “Makine Öğrenmesi İle Ataletsel Navigasyon Sistemlerinde Doğruluğun Geliştirilmesi TT - Improving Accuracy in Inertial Navigation Systems with Machine Learning.” International Journal of Engineering Research and Development 15(1):286–96. doi: 10.29137/umagd.1129097.
• Shu, Mingcong, Guoliang Chen, and Zhenghua Zhang. 2022. “Efficient Image-Based Indoor Localization with MEMS Aid on the Mobile Device.” ISPRS Journal of Photogrammetry and Remote Sensing. doi: 10.1016/j.isprsjprs.2022.01.010.
• Sun, Rui, Zixuan Zhang, Qi Cheng, and Washington Yotto Ochieng. 2022. “Pseudorange Error Prediction for Adaptive Tightly Coupled GNSS/IMU Navigation in Urban Areas.” GPS Solutions. doi: 10.1007/s10291-021-01213-z.
• Sun, Yaowen, Zengke Li, Zhehua Yang, Kefan Shao, and Wangqi Chen. 2022. “Motion Model-Assisted GNSS/MEMS-IMU Integrated Navigation System for Land Vehicle.” GPS Solutions. doi: 10.1007/s10291-022-01318-z.
• Ulgen, E. Kaan. 2024. “Makine Öğrenimi Bölüm-2 (k-En Yakın Komşuluk).” Retrieved March 1, 2024 (https://medium.com/@k.ulgen90/makine-öğrenimi-bölüm-2-6d6d120a18e1).
• Wagstaff, Brandon, and Jonathan Kelly. 2018. “LSTM-Based Zero-Velocity Detection for Robust Inertial Navigation.” in IPIN 2018 - 9th International Conference on Indoor Positioning and Indoor Navigation.
• Wang, Dongsheng, Yongjie Lu, Lei Zhang, and Guoping Jiang. 2019. “Intelligent Positioning for a Commercial Mobile Platform in Seamless Indoor/Outdoor Scenes Based on Multi-Sensor Fusion.” Sensors (Switzerland). doi: 10.3390/s19071696.
• Wang, Sen, Ronald Clark, Hongkai Wen, and Niki Trigoni. 2017. “DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks.” in Proceedings - IEEE International Conference on Robotics and Automation.
• Xing, Huiming, Yu Liu, Shuxiang Guo, Liwei Shi, Xihuan Hou, Wenzhi Liu, and Yan Zhao. 2021. “A Multi-Sensor Fusion Self-Localization System of a Miniature Underwater Robot in Structured and GPS-Denied Environments.” IEEE Sensors Journal. doi: 10.1109/JSEN.2021.3120663.
• Yaman, Orhan, Beyda Tasar, and Oguz Yakut. 2022. “Regression-Based Position Detection for Navigation Using IMU.” in 2022 International Conference on Decision Aid Sciences and Applications, DASA 2022.