  Sampling-based methods such as Rapidly-exploring Random Trees (RRTs) have
been widely used for generating motion paths for autonomous mobile systems. In
this work, we extend time-based RRTs with Control Barrier Functions (CBFs) to
generate, safe motion plans in dynamic environments with many pedestrians. Our
framework is based upon a human motion prediction model which is well suited
for indoor narrow environments. We demonstrate our approach on a high-fidelity
model of the Toyota Human Support Robot navigating in narrow corridors. We show
in three scenarios that our proposed online method can navigate safely in the
presence of moving agents with unknown dynamics.
