Dynamic Log-Gaussian Process Control Barrier Function for Safe Robotic Navigation in Dynamic Environments

TL;DR

This paper introduces a novel Gaussian Process-based control barrier function that enables real-time, obstacle-aware safe navigation for robots in dynamic environments, improving safety and responsiveness.

Contribution

It proposes the DLGP-CBF, a new formulation that models obstacle motion and generates smooth, informative safety barriers using a logarithmic transformation of Gaussian Processes.

Findings

Enhanced obstacle avoidance with larger safety margins

Smoother robot trajectories in dynamic scenarios

Improved responsiveness to obstacle motion

Abstract

Control Barrier Functions (CBFs) have emerged as efficient tools to address the safe navigation problem for robot applications. However, synthesizing informative and obstacle motion-aware CBFs online using real-time sensor data remains challenging, particularly in unknown and dynamic scenarios. Motived by this challenge, this paper aims to propose a novel Gaussian Process-based formulation of CBF, termed the Dynamic Log Gaussian Process Control Barrier Function (DLGP-CBF), to enable real-time construction of CBF which are both spatially informative and responsive to obstacle motion. Firstly, the DLGP-CBF leverages a logarithmic transformation of GP regression to generate smooth and informative barrier values and gradients, even in sparse-data regions. Secondly, by explicitly modeling the DLGP-CBF as a function of obstacle positions, the derived safety constraint integrates predicted obstacle velocities, allowing the controller to proactively respond to dynamic obstacles' motion. Simulation results demonstrate significant improvements in obstacle avoidance performance, including increased safety margins, smoother trajectories, and enhanced responsiveness compared to baseline methods.