Safe Navigation in Human Occupied Environments Using Sampling and Control Barrier Functions
This addresses the problem of robot safety in human-occupied indoor spaces, but it is incremental as it builds on existing sampling and barrier function methods.
The paper tackled safe navigation for robots in dynamic environments with pedestrians by extending time-based RRTs with Control Barrier Functions, demonstrating on a Toyota Human Support Robot model that it can navigate safely in narrow corridors with moving agents.
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.