Sensor-based Distributionally Robust Control for Safe Robot Navigation in Dynamic Environments

Kehan Long    Yinzhuang Yi    Zhirui Dai    Sylvia Herbert    Jorge Cortés    Nikolay Atanasov
Contextual Robotics Institute
University of California, San Diego
Under Review (IJRR), 2024
arXiv Code

1st View Experiment Video (4x speed)

A distributionally robust safe autonomy formulation that directly processes sensor measurements

Abstract

We introduce a novel method for safe mobile robot navigation in dynamic, unknown environments, utilizing onboard sensing to impose safety constraints without the need for accurate map reconstruction. Traditional methods typically rely on detailed map information to synthesize safe stabilizing controls for mobile robots, which can be computationally demanding and less effective, particularly in dynamic operational conditions. By leveraging recent advances in distributionally robust optimization, we develop a distributionally robust control barrier function (DR-CBF) constraint that directly processes range sensor data to impose safety constraints. Coupling this with a control Lyapunov function (CLF) for path tracking, we demonstrate that our CLF-DR-CBF control synthesis method achieves safe, efficient, and robust navigation in uncertain dynamic environments. We demonstrate the effectiveness of our approach in simulated and real autonomous robot navigation experiments, marking a substantial advancement in real-time safety guarantees for mobile robots.

System Overview

Image 1
System Overview
Image 2
CLF Path Follower
Image 3
DR-CBF Safety

Real-world Adversarial Obstacles (1x speed)

Real-world Experiment

Dynamic Environment

Simulated Environment

Performance Under Different Sensor Noise Level

LiDAR noise σ = 0.001
LiDAR noise σ = 0.05
LiDAR noise σ = 0.1

BibTeX

@article{long2024sensorbased,
  title={Sensor-Based Distributionally Robust Control for Safe Robot Navigation in Dynamic Environments}, 
  author={Kehan Long and Yinzhuang Yi and Zhirui Dai and Sylvia Herbert and Jorge Cortés and Nikolay Atanasov},
  journal={arXiv preprint arXiv:2405.18251},
  year={2024}
}