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

We introduce a novel method for safe mobile robot navigation in dynamic, unknown environments, utilizing onboard sensing to impose real-time safety constraints. Traditional methods typically rely on detailed map information to synthesize safe controls for mobile robots, which can be challenging in such 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 probabilistic safety constraints. This enables collision avoidance control at a higher frequency compared to map updates and path planning, allowing our approach to ensure safety while updating the map at a relatively lower frequency. Our formulation is generalizable to robots with complex shapes and accounts for uncertainties in both sensing and localization. Coupling this with a control Lyapunov function (CLF) for stable path tracking, we propose a CLF-DR-CBF control synthesis method that achieves safe, efficient, and robust navigation in unknown dynamic environments. We demonstrate the effectiveness of our approach in simulated and real autonomous differential-drive robot navigation experiments, marking a substantial advancement in real-time safety guarantees for mobile robots.