Comparison of classification models and accelerometer sensors for VTOL UAV flight condition detection

This study develops a classification model for detecting multiple flight conditions of VTOL (Vertical Take-Off and Landing) UAVs using accelerometer data, with a motion capture system included for comparison. The objective is to identify the most effective machine learning model for classifying various flight conditions, such as healthy and faulty propellers, different payloads, and windy environments. Initially, various machine learning models, including Quadratic Support Vector Machine (QSVM), Neural Networks, and Naive Bayes, were trained using acceleration and displacement data. QSVM was identified as the best-performing model, achieving 87.5% training accuracy with acceleration data and 79.3% with displacement data. Following this, data from two accelerometers (an iPhone SE 2020 and an ADXL345) were used exclusively with the QSVM model for further comparison. The iPhone SE sensor achieved 97.73% training accuracy, while the ADXL345 attained 93.06%. While the iPhone sensor demonstrates superior performance, it serves only as a benchmark, as it is not intended for onboard UAV applications. The results indicate that affordable sensors, like the ADXL345, can achieve sufficient accuracy, making them viable for practical UAV deployments. The study concludes by recommending higher-quality sensors and advanced machine learning techniques for enhanced UAV fault detection.

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