Autonomous UAV Volcanic Plume Sampling Based on Machine Vision and Path Planning

Drones currently serve as a valuable tool for in-situ sampling of volcanic plumes, but they still involve manual piloting. In this paper, we enable autonomous dual plume sampling by using a machine vision model to detect eruptions. When an eruption is detected, a sampling trajectory is automatically generated to intercept the plume twice to collect comparative samples. The machine vision model is developed by training a YOLOv8 object detection model thanks to a database of 1505 images that feature labelled plumes. The obtained average precision value of the model's plume class, at 90.7%, is comparable to that of state-of-the-art models for wildfire smoke monitoring. The performance of this method is assessed using a software-in-the-loop simulation of the drone and a simulated plume model. Although the results confirm the efficacy of using a machine vision model for triggering an onboard path-planning algorithm, it also suggests the potential for a hybrid strategy that integrates visual servoing with our proposed path-planning approach.