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

TL;DR

This paper presents a distributionally robust control method for safe robot navigation in dynamic environments, integrating sensor data and probabilistic safety constraints to ensure robustness and real-time operation.

Contribution

It introduces a novel distributionally robust control barrier function (DR-CBF) framework that handles noisy sensor data and complex robot dynamics for safe navigation.

Findings

Effective in simulation and real-world experiments

Ensures safety under sensor noise and environmental uncertainty

Operates at real-time control frequencies

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.