用声音风险信号动态调整机器人安全距离，提升工地导航安全性。
Control Barrier Functions with Audio Risk Awareness for Robot Safe Navigation on Construction Sites
- 用音频信号检测锤击声，实时生成风险提示
- 安全边界随声音风险动态调整，减少碰撞
- 适合高动态、视觉受限的工地自动驾驶场景
施工自动化日益需要自主移动机器人，但复杂多变且常有视线遮挡的工地环境使感知与导航极具挑战。当前多数自主系统未充分利用音频信息。本文提出一种基于控制屏障函数（CBF）的安全过滤器，通过音频衍生的风险信号动态调整避障安全裕度。该框架引入轻量级实时锤击声检测器，基于信号包络与周期性特征实现，其输出作为外部风险输入，直接用于调节屏障函数。在仿真中使用两种CBF形式（圆形与目标对齐椭圆）对单轮机器人进行测试，结果表明：所有试验均无安全违规，目标到达率分别为40.2%（圆形）与76.5%（椭圆），后者因更优路径规划避免死锁。该融合音频感知的CBF控制器为动态高危环境中的机器人安全决策提供了多模态推理新路径。
原文摘要 · Abstract (English)
Construction automation increasingly requires autonomous mobile robots, yet robust autonomy remains challenging on construction sites. These environments are dynamic and often visually occluded, which complicates perception and navigation. In this context, valuable information from audio sources remains underutilized in most autonomy stacks. This work presents a control barrier function (CBF)-based safety filter that provides safety guarantees for obstacle avoidance while adapting safety margins during navigation using an audio-derived risk cue. The proposed framework augments the CBF with a lightweight, real-time jackhammer detector based on signal envelope and periodicity. Its output serves as an exogenous risk that is directly enforced in the controller by modulating the barrier function. The approach is evaluated in simulation with two CBF formulations (circular and goal-aligned elliptical) with a unicycle robot navigating a cluttered construction environment. Results show that the CBF safety filter eliminates safety violations across all trials while reaching the target in 40.2% (circular) vs. 76.5% (elliptical), as the elliptical formulation better avoids deadlock. This integration of audio perception into a CBF-based controller demonstrates a pathway toward richer multimodal safety reasoning in autonomous robots for safety-critical and dynamic environments.
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