Safe Navigation in Dynamic Environments Using Data-Driven Koopman Operators and Conformal Prediction

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.

@article{liang2025_2504.00352,
  title={ Safe Navigation in Dynamic Environments Using Data-Driven Koopman Operators and Conformal Prediction },
  author={ Kaier Liang and Guang Yang and Mingyu Cai and Cristian-Ioan Vasile },
  journal={arXiv preprint arXiv:2504.00352},
  year={ 2025 }
}