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

TL;DR

This paper introduces a novel method combining sampling-based planning with control barrier functions to enable safe navigation of robots in dynamic, pedestrian-rich indoor environments.

Contribution

It extends time-based RRTs with CBFs and a human motion prediction model for safe, online robot navigation amidst moving pedestrians.

Findings

Successfully navigates in narrow corridors with pedestrians

Demonstrates safety in dynamic environments with unknown agent behaviors

Validates approach on high-fidelity robot simulation

Abstract

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.