用信念控制屏障函数提升机器人在动态环境中的安全性和抗干扰能力。
Risk-Aware Robot Control in Dynamic Environments Using Belief Control Barrier Functions
- 基于样本信念分布设计新型控制屏障函数，融合尾部风险集中界。
- 在1kHz实时性下实现水下机器人目标跟踪与动态避障，性能稳定。
- 适合高风险场景的自主机器人控制，尤其适用于感知不确定性强的系统。
在动态环境中保障自主机器人安全面临诸多挑战，如未建模动力学、噪声传感器测量和部分可观测性。为应对这些限制，通常需对真实状态维护一个信念分布，该分布可采用非参数化、基于样本的表示以更灵活地刻画不确定性。本文提出一种新型信念控制屏障函数（BCBFs），专为在随机动力学及基于样本的环境状态信念下确保动态环境中的安全性而设计。所提方法将尾部风险度量的可证明集中界融入BCBFs，有效处理由样本表示的多模态和偏斜信念分布。此外，该方法对分布漂移具有鲁棒性，其影响在预设范围内。通过两个模拟的水下机器人应用——目标跟踪与动态避碰——验证了该方法的有效性与实时性能（约1kHz）。
原文摘要 · Abstract (English)
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.
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