This paper addresses the problem of safety-critical control of autonomous robots, considering the ubiquitous uncertainties arising from unmodeled dynamics and noisy sensors. To take into account these uncertainties, probabilistic state estimators are often deployed to obtain a belief over possible states. Namely, Particle Filters (PFs) can handle arbitrary non-Gaussian distributions in the robot's state. In this work, we define the belief state and belief dynamics for continuous-discrete PFs and construct safe sets in the underlying belief space. We design a controller that provably keeps the robot's belief state within this safe set. As a result, we ensure that the risk of the unknown robot's state violating a safety specification, such as avoiding a dangerous area, is bounded. We provide an open-source implementation as a ROS2 package and evaluate the solution in simulations and hardware experiments involving high-dimensional belief spaces.

翻译：本文针对自主机器人在安全关键控制中面临的问题，考虑了由未建模动态和含噪传感器引起的普遍不确定性。为应对这些不确定性，常采用概率状态估计器获取关于可能状态的置信，其中粒子滤波器能够处理机器人状态中任意非高斯分布。本研究定义了连续-离散粒子滤波器的置信状态与置信动力学，并在底层置信空间中构建了安全集。我们设计了一种控制器，可证明性地将机器人的置信状态维持在安全集内，从而确保未知机器人状态违反安全规范（如避开危险区域）的风险有界。文中提供了基于ROS2软件包的开源实现，并在涉及高维置信空间的仿真与硬件实验中评估了该方案。