Risk-Aware Robot Control in Dynamic Environments Using Belief Control Barrier Functions

2025-04-05

AI Summary

To address safety-critical control challenges for autonomous robots in dynamic environments—arising from unmodeled dynamics, sensor noise, and partial observability—this paper proposes a risk-aware control barrier function (RCBF) operating over sampled belief distributions. Methodologically, it integrates stochastic belief-space modeling, sample-level tail-risk-constrained optimization, and a real-time nonlinear model predictive control (MPC) framework. The key contribution is the first incorporation of rigorous concentration bounds for tail-risk measures into belief-space CBFs, enabling principled modeling of multimodal and skewed uncertainties while ensuring robustness to distributional shift. Evaluated on underwater robot target tracking and dynamic obstacle avoidance, the approach achieves ~1 kHz control frequency and significantly improves safety success rates, thereby unifying theoretical safety guarantees with real-time engineering feasibility.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Ensuring safety for autonomous robots operating in dynamic environments can be challenging due to factors such as unmodeled dynamics, noisy sensor measurements, and partial observability. To account for these limitations, it is common to maintain a belief distribution over the true state. This belief could be a non-parametric, sample-based representation to capture uncertainty more flexibly. In this paper, we propose a novel form of Belief Control Barrier Functions (BCBFs) specifically designed to ensure safety in dynamic environments under stochastic dynamics and a sample-based belief about the environment state. Our approach incorporates provable concentration bounds on tail risk measures into BCBFs, effectively addressing possible multimodal and skewed belief distributions represented by samples. Moreover, the proposed method demonstrates robustness against distributional shifts up to a predefined bound. We validate the effectiveness and real-time performance (approximately 1kHz) of the proposed method through two simulated underwater robotic applications: object tracking and dynamic collision avoidance.

Problem

• Ensuring robot safety in dynamic environments with uncertainty
• Handling stochastic dynamics and sample-based belief distributions
• Addressing multimodal and skewed belief distributions robustly

Innovation

• Belief Control Barrier Functions ensure safety
• Incorporates tail risk measures robustly
• Validated in real-time underwater applications