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.

翻译：我们提出了一种新颖的动态环境安全导航框架，通过将Koopman算子理论与保形预测相结合。该方法利用数据驱动的Koopman近似学习非线性动力学，并采用保形预测量化不确定性，为近似误差提供统计保证。这种不确定性通过约束紧缩有效地融入模型预测控制器（MPC）框架，确保鲁棒的安全保证。我们实现了一种分层控制架构，其中参考生成器为安全导航提供航点。仿真实验验证了所提方法的有效性。