用粒子滤波建模多目标不确定性，实现水下机器人安全控制
Risk-Aware Belief Control Barrier Functions over Random Finite Sets
- 基于粒子集表示目标状态不确定性，构建风险感知的控制屏障函数
- 在连续预测中保证安全集合前向不变，离散更新时满足显式安全条件
- 适用于动态环境中需实时避障的机器人系统，如水下探测任务
在未知、动态环境中保障机器人安全是基本要求，涉及从噪声和不完整测量中推断移动目标的状态及其数量。本文提出一种风险感知的信念控制屏障函数（BCBF）框架，以应对多目标状态不确定性。该不确定性由随机有限集（RFS）信念表征，通过序列蒙特卡洛概率假设密度（SMC-PHD）滤波器用一组粒子进行估计。直接基于这些粒子构造非光滑的BCBF，建立连续预测下的安全集合前向不变性，并推导出离散更新保持安全性的显式条件。仿真与真实水下实验验证了该方法的有效性与高效性。
原文摘要 · Abstract (English)
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.
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