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

Traditional approaches using lidar or stereo cameras are resource-intensive, making them impractical for lightweight UAVs. This blog explores a novel event-based adaptive Koopman framework that uses optic flow data from a monocular camera to achieve precise landings with minimal computational overhead.

Challenges in UAV Landing

Autonomous UAV landings face several obstacles:

• Unreliable altitude estimation: Many UAVs lack dedicated sensors for altitude tracking.
• Ground effect disturbances: Changes in aerodynamics near the ground affect stability.
• Unknown platform motion: Moving platforms introduce uncertainties in trajectory planning.

Traditional learning-based approaches struggle with generalization, while existing model-based methods require extensive computational resources.

The Koopman-Based Solution

This study introduces a Koopman operator theory-based approach, which enables a global linear representation of the nonlinear optic flow dynamics. The key contributions include:

• Offline Koopman Model Learning: Uses Extended Dynamic Mode Decomposition (EDMD) to derive a linear approximation from optic flow data.
• Online Model Adaptation: An adaptation law updates the Koopman model in real-time, minimizing errors caused by ground effects and platform motion.
• Event-Triggered Control (ETC): Regulates optic flow-based trajectory tracking while minimizing unnecessary updates, reducing computational load.

Performance and Validation

The proposed method was tested in simulation under realistic conditions, including sensor noise and unknown platform motion. Key findings include:

• Smooth and precise landings: UAVs successfully track desired optic flow references, ensuring soft touchdowns.
• 33% reduction in computational overhead: Compared to time-triggered control, ETC significantly optimizes processing efficiency.
• Robust performance under ground effect and motion disturbances.

Conclusion

This event-driven Koopman-based framework represents a major advancement in UAV landing technology, allowing efficient and adaptive control using only monocular vision. Future research will focus on real-world hardware implementation to validate the approach under experimental conditions.

What are your thoughts on data-driven UAV control strategies? Let’s discuss in the comments!