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

TL;DR

This paper introduces an adaptive Koopman-based control framework enabling resource-constrained UAVs to perform smooth landings on moving platforms using optic flow, with online adaptation and event-driven control to handle uncertainties and reduce computation.

Contribution

It develops a novel online adaptation scheme within the Koopman framework and integrates event-based triggering into MPC for efficient, robust landing on dynamic platforms.

Findings

Demonstrates robustness under platform motion and sensor noise

Ensures global convergence with Zeno-free behavior

Outperforms non-adaptive schemes in simulations

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.