信念制御バリア関数による動的環境でのリスク対応ロボット制御
Risk-Aware Robot Control in Dynamic Environments Using Belief Control Barrier Functions
サンプルベースの信念分布とテールリスクの集中不等式を組み込んだ信念制御バリア関数を提案し、動的環境での安全なロボット制御を実現した。
著者: Shaohang Han, Matti Vahs, Jana Tumova
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
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.
