Safe Navigation in Dynamic Environments Using Data-Driven Koopman Operators and Conformal Prediction
Year: 2025
Type: preprint
Abstract: We propose a novel framework for safe navigation in dynamic environments by integrating Koopman operator theory with conformal prediction. Our approach leverages data-driven Koopman approximation to learn nonlinear dynamics and employs conformal prediction to quantify uncertainty, providing statistical guarantees on approximation errors. This uncertainty is effectively incorporated into a Model Predictive Controller (MPC) formulation through constraint tightening, ensuring robust safety guarantees. We implement a layered control architecture with a reference generator providing waypoints for safe navigation. The effectiveness of our methods is validated in simulation.
Source: arXiv (Cornell University)
Authors: Liang, Kaier, Yang, Guang, Cai, Mingyu, Vasile, Cristian-Ioan
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6966746013
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