Safe Navigation under State Uncertainty: Online Adaptation for Robust Control Barrier Functions
Ersin Das, Rahal Nanayakkara, Xiao Tan, Ryan Bena, Joel Burdick, Paulo Tabuada, Aaron Ames
AI summary
Problem
Existing robust control barrier function methods often impose overly conservative safety margins or suffer from infeasibility when handling state estimation errors. Additionally, managing multiple safety constraints and dual relative degree dynamics remains computationally challenging for real-world vehicle tracking.
Approach
The authors develop an optimization-based online adaptation method to dynamically tune robustness parameters, minimizing safe set inflation. They unify multiple safety constraints into a single control barrier function using Poisson’s equation and address dual relative degree issues to enable practical safe tracking.
Key results
- Online optimization of R-CBF robustness parameters to minimize safe set inflation
- Unification of multiple safety constraints into a single CBF via Poisson’s equation
- Resolution of dual relative degree issues for unicycle system tracking
- Hardware validation on a tracked mobile robot demonstrating improved performance over baseline formulations
Why it matters
Enables reliable, less conservative safety-critical control for autonomous robots operating with imperfect state estimates, directly benefiting real-world navigation and autonomy applications.
Abstract
Measurements and state estimates are often im- perfect 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 im- posed strict conditions on the input. These methods can be overly conservative and can introduce issues such as infeasibil- ity, 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.