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.
Autonomous UAV Volcanic Plume Sampling Based on Machine Vision and Path Planning
04.06.2024
4139993 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Autonomous volcanic plume detection on planetary bodies
Online Contents | 2014
|Springer Verlag | 2020
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