CBF-based Probabilistic Safe Navigation under Unknown Nonlinear Obstacle Dynamics
It addresses safe navigation for autonomous vehicles under dynamic obstacles with unknown nonlinear dynamics, providing formal probabilistic safety guarantees.
The paper proposes a data-driven observer for unknown obstacle dynamics that generates an alpha-confidence set flow, transformed into a Control Barrier Function to enforce probabilistic safety. The method accommodates nonlinear ego vehicle dynamics of arbitrary relative degree, demonstrated on an unmanned surface vehicle.
Safe navigation for an ego vehicle in uncertain environments characterized by dynamic obstacles with unknown nonlinear dynamics is a challenging problem of significant practical interest. Existing approaches in the literature either lack formal safety guarantees, require full model knowledge, or fail to account for the risk associated with the vehicle's exact body geometry and the temporal evolution of uncertainty between sampling instants. In this paper, we propose a data-driven observer for the unknown obstacle dynamics that generates an alpha-confidence set flow, which is exactly transformed into a Control Barrier Function (CBF) to enforce (1-alpha)-probability safety. The proposed framework accommodates nonlinear ego vehicle dynamics of arbitrary relative degree, as demonstrated through case studies involving first- and second-order dynamics of an unmanned surface vehicle.