CBF-based Probabilistic Safe Navigation under Unknown Nonlinear Obstacle Dynamics

TL;DR

This paper introduces a data-driven control barrier function approach for safe navigation of vehicles in environments with unknown nonlinear obstacle dynamics, providing probabilistic safety guarantees.

Contribution

It develops a novel observer that creates confidence sets for obstacle dynamics, enabling CBF-based safety enforcement under uncertainty.

Findings

The method guarantees (1-alpha) probability safety in uncertain environments.

Demonstrated effectiveness on unmanned surface vehicle models with nonlinear dynamics.

Provides a framework accommodating arbitrary relative degree of vehicle dynamics.

Abstract

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.