Ensuring safety in real-world robotic systems is often challenging due to unmodeled disturbances and noisy sensor measurements. To account for such stochastic uncertainties, many robotic systems leverage probabilistic state estimators such as Kalman filters to obtain a robot's belief, i.e. a probability distribution over possible states. We propose belief control barrier functions (BCBFs) to enable risk-aware control synthesis, leveraging all information provided by state estimators. This allows robots to stay in predefined safety regions with desired confidence under these stochastic uncertainties. BCBFs are general and can be applied to a variety of robotic systems that use extended Kalman filters as state estimator. We demonstrate BCBFs on a quadrotor that is exposed to external disturbances and varying sensing conditions. Our results show improved safety compared to traditional state-based approaches while allowing control frequencies of up to 1kHz.

翻译：在真实世界机器人系统中，因未建模扰动和含噪传感器测量，确保安全性常具挑战性。为应对此类随机不确定性，许多机器人系统采用卡尔曼滤波器等概率状态估计器获取机器人的信念（即可能状态的概率分布）。我们提出信念控制障碍函数（BCBFs），通过充分利用状态估计器提供的全部信息，实现风险感知控制综合。这使得机器人能在随机不确定性下，以期望置信度保持在预定义安全区域内。BCBFs具有通用性，可应用于使用扩展卡尔曼滤波器作为状态估计器的各类机器人系统。我们在暴露于外部扰动和变化感知条件下的四旋翼飞行器上验证了BCBFs。结果表明，与传统基于状态的方法相比，该方法在提升安全性的同时，仍能支持高达1kHz的控制频率。