Enhancement of multirotor UAV conceptual design through Machine Learning algorithms

Abstract Designing an efficient and optimized multirotor UAV requires laborious trade-off analyses, involving numerous design variables and mission requirement parameters, especially during the early conceptual design phase. The large number of unknown parameters, as well as the associated design effort often leads to non-optimal designs, for the sake of time efficiency. This work presents the implementation of a machine learning (ML) framework to assist and expedite the conceptual design phase of multirotor UAVs. The framework utilizes information from a comprehensive database of commercial lightweight multirotor UAVs. The database contains an extensive collection of crucial sizing parameters, performance metrics, and features associated with foldability and indoor guidance (e.g., obstacle avoidance sensors). These attributes specifically pertain to multirotor UAVs weighing less than 2kg, which exhibit diverse design and performance characteristics. The proposed ML framework employs multiple regression models (e.g. k-nearest neighbors regression, multi-layer perceptron regression) to predict the sizing parameters during a multirotor UAV’s conceptual design phase. This enables designers to make quick informed decisions, while also significantly reducing computational time and effort. Finally, the ML framework’s predictive capability is validated by comparing the predicted values with real-world data from an “unseen” test dataset.