Result: A Novel Simulation-Driven Data Enrichment Approach to Improve Machine Learning Algorithm Performance

This study presents a novel framework for developing machine learning algorithms by integrating the Robot Operating System (ROS) with the Unity platform for UAV research. The framework leverages ROS’s robust algorithm libraries and multi-language support, combined with Unity’s capacity to create realistic simulations, allowing for comprehensive model testing without relying on physical experiments. It provides a high-fidelity simulation environment replicating real-world scenarios, enabling the validation of UAVs in complex conditions that are difficult to replicate physically. Additionally, the framework offers an innovative approach to generating enriched datasets by capturing object data from various perspectives and incorporating contextual information, enhancing models’ object detection in diverse scenarios. To validate its capabilities, we conducted two case studies: the first targets victim detection in rescue operations, showing that our generated dataset poses a higher challenge compared to COCO and PASCAL SOC when applied to multiple models. The second study assesses UAV performance in obstacle detection, collision avoidance, and navigation after training on our dataset. The findings demonstrate that this framework accelerates AI model development and serves as a reliable platform for validating UAV operations, making it a valuable asset for advancing UAV research