发表机构
KTH Royal Institute of Technology; Delft University of Technology（皇家理工学院; 代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究在未知动态环境中确保机器人安全的问题，利用风险感知信念控制障碍函数框架，基于随机有限集信念及相关滤波器，构建非光滑BCBF，经仿真和水下实验验证了该方法的有效性与效率。
AI 中文摘要
确保机器人在未知动态环境中的安全是一项基本要求。这涉及从噪声大、不完整的测量中推断未知且数量随时间变化的移动物体的状态。我们使用风险感知信念控制障碍函数（BCBF）框架来解决在诱导的多目标状态不确定性下的安全控制问题。不确定性由随机有限集（RFS）信念捕获，通过顺序蒙特卡罗概率假设密度（SMC-PHD）滤波器估计，该滤波器用一组粒子表示它。直接基于这些粒子，我们构建了一个非光滑的BCBF，建立了连续预测下安全集的前向不变性，并推导了离散更新保持安全性的明确条件。仿真和实际水下实验证明了该方法的有效性和效率。
英文摘要
Ensuring robot safety in unknown, dynamic environments is a fundamental requirement. It involves inferring the states of an unknown and time-varying number of moving objects from noisy, incomplete measurements. We address safe control under the induced multi-object state uncertainty with a risk-aware belief control barrier function (BCBF) framework. The uncertainty is captured by a random finite set (RFS) belief, estimated by a sequential Monte Carlo probability hypothesis density (SMC-PHD) filter that represents it with a set of particles. Building directly on these particles, we construct a nonsmooth BCBF, establish forward invariance of the safe set under continuous prediction, and derive an explicit condition under which discrete updates preserve safety. Simulation and real-world underwater experiments demonstrate the effectiveness and efficiency of the proposed approach.
