Point Cloud-Based Control Barrier Function Regression for Safe and Efficient Vision-Based Control

Massimiliano De Sa; Prasanth Kotaru; Koushil Sreenath

ICRA 2024poster9 citations

Point Cloud-Based Control Barrier Function Regression for Safe and Efficient Vision-Based Control

Massimiliano De Sa, Prasanth Kotaru, Koushil Sreenath

Abstract

Control barrier functions have become an increasingly popular framework for safe real-time control. In this work, we present a computationally low-cost framework for synthesizing barrier functions over point cloud data for safe vision-based control. We take advantage of surface geometry to locally define and synthesize a quadratic CBF over a point cloud. This CBF is used in a CBF-QP for control and verified in simulation on quadrotors and in hardware on quadrotors and the TurtleBot3. This technique enables safe navigation through unstructured and dynamically changing environments and is shown to be significantly more efficient than current methods.

BibTeX

@inproceedings{icra2024_pointcloudbasedc,
  title = {Point Cloud-Based Control Barrier Function Regression for Safe and Efficient Vision-Based Control},
  author = {Massimiliano De Sa and Prasanth Kotaru and Koushil Sreenath},
  booktitle = {ICRA 2024},
  year = {2024}
}