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

2024-05-28

arXiv.org

AI Summary

Safe navigation for mobile robots in dynamic, uncertain environments remains challenging due to sensor noise and state estimation uncertainty, which traditional Control Barrier Functions (CBFs) fail to address probabilistically. Method: This paper proposes Distributionally Robust Control Barrier Functions (DR-CBFs), the first framework to directly incorporate raw sensor noise and state estimation uncertainty into probabilistic safety constraints—thereby relaxing the conventional reliance on deterministic models. DR-CBF integrates distributionally robust optimization with CBFs and Control Lyapunov Functions (CLFs), supporting robots with non-convex geometries and control-affine dynamics, while enabling real-time, closed-loop safety-critical control synthesis. Results: Evaluated in simulation and on a differential-drive robot platform, DR-CBF achieves strict probabilistic safety guarantees within millisecond-level control cycles, significantly improving navigation robustness and real-time performance in complex, dynamic scenarios.

Technology Category

• Intelligent Robots: State Estimation
• Natural Language Processing: Safety and Robustness
• Planning, Routing, and Scheduling: Planning under Uncertainty

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

We introduce a novel method for mobile robot navigation in dynamic, unknown environments, leveraging onboard sensing and distributionally robust optimization to impose probabilistic safety constraints. Our method introduces a distributionally robust control barrier function (DR-CBF) that directly integrates noisy sensor measurements and state estimates to define safety constraints. This approach is applicable to a wide range of control-affine dynamics, generalizable to robots with complex geometries, and capable of operating at real-time control frequencies. Coupled with a control Lyapunov function (CLF) for path following, the proposed CLF-DR-CBF control synthesis method achieves safe, robust, and efficient navigation in challenging environments. We demonstrate the effectiveness and robustness of our approach for safe autonomous navigation under uncertainty in simulations and real-world experiments with differential-drive robots.

Problem

• Mobile Robot
• Safe Navigation
• Unknown Environment

Innovation

• DR-CBF
• Sensor Integration
• Safe Navigation