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

Cited By: 1

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

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