Safe Navigation under State Uncertainty: Online Adaptation for Robust Control Barrier Functions

TL;DR

This paper introduces an online adaptation method for robust control barrier functions to enhance safety in vehicle navigation under uncertain state estimates, reducing conservativeness and improving performance.

Contribution

It presents a novel online parameter adaptation scheme for R-CBFs, merging safety constraints via Poisson's equation, and addresses dual relative degree issues for better vehicle tracking.

Findings

Improved safety and navigation performance in experiments.

Reduced conservativeness compared to existing R-CBF methods.

Enhanced feasibility and control efficiency in obstacle avoidance.

Abstract

Measurements and state estimates are often imperfect in control practice, posing challenges for safety-critical applications, where safety guarantees rely on accurate state information. In the presence of estimation errors, several prior robust control barrier function (R-CBF) formulations have imposed strict conditions on the input. These methods can be overly conservative and can introduce issues such as infeasibility, high control effort, etc. This work proposes a systematic method to improve R-CBFs, and demonstrates its advantages on a tracked vehicle that navigates among multiple obstacles. A primary contribution is a new optimization-based online parameter adaptation scheme that reduces the conservativeness of existing R-CBFs. In order to reduce the complexity of the parameter optimization, we merge several safety constraints into one unified numerical CBF via Poisson's equation. We further address the dual relative degree issue that typically causes difficulty in vehicle tracking. Experimental trials demonstrate the overall performance improvement of our approach over existing formulations.