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Sampling-based methods such as Rapidly-exploring Random Trees (RRTs) havebeen widely used for generating motion paths for autonomous mobile systems. Inthis work, we extend time-based RRTs with Control Barrier Functions (CBFs) togenerate, safe motion plans in dynamic environments with many pedestrians. Ourframework is based upon a human motion prediction model which is well suitedfor indoor narrow environments. We demonstrate our approach on a high-fidelitymodel of the Toyota Human Support Robot navigating in narrow corridors. We showin three scenarios that our proposed online method can navigate safely in thepresence of moving agents with unknown dynamics.

Safe Navigation in Human Occupied Environments Using Sampling and Control Barrier Functions