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

2025-08-26

AI Summary

To address the excessive conservatism, poor feasibility, and high control burden of robust Control Barrier Functions (R-CBFs) under uncertain state estimation, this paper proposes an online parameter-adaptive R-CBF framework for safe navigation of tracked vehicles in multi-obstacle environments. Methodologically: (1) state estimation errors are explicitly modeled; (2) an optimization-based online adaptation mechanism dynamically tunes CBF parameters to match real-time uncertainty; and (3) multiple safety constraints are unified into a single numerical CBF, with analytical handling of the double-relative-degree issue. Experiments demonstrate that the proposed method significantly improves safety, feasibility, and control efficiency: it guarantees zero collisions while reducing average control energy consumption by 23.6%, outperforming existing R-CBF approaches.

Technology Category

• Intelligent Robots: State Estimation
• Natural Language Processing: Safety and Robustness
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Addresses safe navigation under imperfect state estimation
• Reduces conservativeness in robust control barrier functions
• Solves dual relative degree issues in vehicle tracking

Innovation

• Online parameter adaptation scheme reduces conservativeness
• Merges safety constraints via Poisson's equation
• Addresses dual relative degree in vehicle tracking