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arXiv · 2607.15016

Risk-Aware Belief Control Barrier Functions over Random Finite Sets

Shaohang Han·Gang Chen·Yixi Cai·Ignacio Torroba·Ivan Stenius·Patric Jensfelt·Javier Alonso-Mora·Jana Tumova
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Abstract

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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Shaohang Han, Gang Chen, Yixi Cai, Ignacio Torroba, Ivan Stenius, Patric Jensfelt, Javier Alonso-Mora, Jana Tumova. 2026-07-16. Risk-Aware Belief Control Barrier Functions over Random Finite Sets. https://arxiv.org/abs/2607.15016

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