Neural Control Barrier Functions for Safe Navigation

Key Points

Safe navigation is achieved using control barrier functions and reactive safety filters, ensuring robot safety.
In simulations, the collaborative methodology led to successful navigation without environmental maps or estimates.
The approach integrates a LiDAR sensor on a multirotor platform, testing its effectiveness both in real-world and simulation settings. The findings highlight the potential to develop robust autonomous systems capable of navigating complex environments.

Abstract

Autonomous robot navigation can be particularly demanding, especially when the surrounding environment is not known and safety of the robot is crucial. This work relates to the synthesis of Control Barrier Functions (CBFs) through data for safe navigation in unknown environments. A novel methodology to jointly learn CBFs and corresponding safe controllers, in simulation, inspired by the State Dependent Riccati Equation (SDRE) is proposed. The CBF is used to obtain admissible commands from any nominal, possibly unsafe controller. An approach to apply the CBF inside a safety filter without the need for a consistent map or position estimate is developed. Subsequently, the resulting reactive safety filter is deployed on a multirotor platform integrating a LiDAR sensor both in simulation and real-world experiments.

Read Full Paperexternally

Mark Helpful

Bookmark

Relay

View Full Paper