Dynamic risk assessment and assurance for configurable UAVs (Conference Presentation)

This research centres on a dynamic risk assessment and assurance strategy for object detection technology within a versatile Unmanned Aerial Vehicle (UAV) during surveillance missions. While existing studies focus on enhancing object detection accuracy, this research emphasises the integration of vision systems into autonomous systems with defined objectives. Using stereo cameras with YOLO object detection, the study monitors factors like resource usage, environmental impact, and mission adaptability. Parameters such as slack time, battery levels, weather, and power consumption are continuously monitored and used to dynamically adjust the camera system's operation mode. By utilising formal modelling and verification techniques, the system can adapt to various scenarios, ensuring mission success. Additionally, Goal Structured Notation (GSN) is employed to articulate and visualise mission success arguments, offering a structured framework for assessing evidence and assuring system properties. By addressing uncertainties and visualising them through GSN, the research enhances UAV adaptability and resilience in diverse environments.