Real Time Local Wind Inference for Robust Autonomous Navigation
arXiv:2604.00343v1 Announce Type: new Abstract: This thesis presents a solution that enables aerial robots to reason about surrounding wind flow fields in real time using on board sensors and embedded flight hardware. The core novelty of this research is the fusion of range measurements with sparse in situ wind measurements to predict surrounding flow fields. We aim to address two fundamental questions: first, the sufficiency of topographical data for accurate wind prediction in dense urban environments; and second, the utility of learned wind models for motion planning with an emphasis on energy efficiency and obstacle avoidance. Drawing on tools from deep learning, fluid mechanics, and optimal control, we establish a framework for local wind prediction using navigational LiDAR, and then
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