🤖 AI Summary
To address rare, transient, and precisely localized dynamic scientific phenomena—such as volcanic eruption plumes—this paper proposes an onboard real-time perception–decision–response closed-loop framework. Methodologically, it fuses forward-looking satellite imagery with lightweight CNNs and traditional machine learning models for edge-based plume detection, and integrates multi-objective trajectory planning to autonomously generate optimal high-resolution sensor pointing paths. Its key contribution lies in the first deep integration of event-driven real-time detection and online trajectory planning on an edge computing platform, enabling fully autonomous onboard observation scheduling. Simulation results demonstrate that, compared to baseline approaches, the framework achieves over a tenfold increase in scientific return, while total inference and planning latency remains below typical revisit intervals—substantially improving capture probability and data value for sparse dynamic events.
📝 Abstract
Advancements in onboard computing mean remote sensing agents can employ state-of-the-art computer vision and machine learning at the edge. These capabilities can be leveraged to unlock new rare, transient, and pinpoint measurements of dynamic science phenomena. In this paper, we present an automated workflow that synthesizes the detection of these dynamic events in look-ahead satellite imagery with autonomous trajectory planning for a follow-up high-resolution sensor to obtain pinpoint measurements. We apply this workflow to the use case of observing volcanic plumes. We analyze classification approaches including traditional machine learning algorithms and convolutional neural networks. We present several trajectory planning algorithms that track the morphological features of a plume and integrate these algorithms with the classifiers. We show through simulation an order of magnitude increase in the utility return of the high-resolution instrument compared to baselines while maintaining efficient runtimes.