用数据驱动方法提升动态环境导航安全性
Safe Navigation in Dynamic Environments Using Data-Driven Koopman Operators and Conformal Prediction
- 结合柯尔莫哥洛夫算子与置信预测，学习非线性动态
- 通过约束收紧实现不确定性量化，保障控制安全
- 适合需高可靠性的自动驾驶与机器人导航场景
我们提出一种新框架，通过将柯尔莫哥洛夫算子理论与置信预测相结合，实现动态环境中的安全导航。该方法利用数据驱动的柯尔莫哥洛夫近似来学习非线性动力学，并采用置信预测量化近似误差，提供统计意义上的误差保证。这些不确定性被有效融入模型预测控制器（MPC）中，通过约束收紧实现鲁棒的安全性保障。我们设计了分层控制架构，其中参考生成器提供安全导航的路径点。所提方法在仿真中得到验证。
原文摘要 · Abstract (English)
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.
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