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arXiv · 2501.16868

Event-Based Adaptive Koopman Framework for Optic Flow-Guided Landing on Moving Platforms

Bazeela Banday·Chandan Kumar Sah·Jishnu Keshavan
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Abstract

This paper presents an optic flow-guided approach for achieving soft landings by resource-constrained unmanned aerial vehicles (UAVs) on dynamic platforms. An offline data-driven linear model based on Koopman operator theory is developed to describe the underlying (nonlinear) dynamics of optic flow output obtained from a single monocular camera that maps to vehicle acceleration as the control input. Moreover, a novel adaptation scheme within the Koopman framework is introduced online to handle uncertainties such as unknown platform motion and ground effect, which exert a significant influence during the terminal stage of the descent process. Further, to minimize computational overhead, an event-based adaptation trigger is incorporated into an event-driven Model Predictive Control (MPC) strategy to regulate optic flow and track a desired reference. A detailed convergence analysis ensures global convergence of the tracking error to a uniform ultimate bound while ensuring Zeno-free behavior. Simulation results demonstrate the algorithm's robustness and effectiveness in landing on dynamic platforms under ground effect and sensor noise, which compares favorably to non-adaptive event-triggered and time-triggered adaptive schemes.

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Bazeela Banday, Chandan Kumar Sah, Jishnu Keshavan. 2025-01-28. Event-Based Adaptive Koopman Framework for Optic Flow-Guided Landing on Moving Platforms. https://arxiv.org/abs/2501.16868

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