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.

翻译：建筑自动化日益需要自主移动机器人，但在建筑工地上实现鲁棒自主性仍然具有挑战性。这些环境动态多变且视觉遮挡严重，这使感知与导航变得复杂。在此背景下，来自音频源的有价值信息在大多数自主系统中仍未得到充分利用。本研究提出了一种基于控制屏障函数的安全滤波器，该滤波器为避障提供安全保证，同时利用音频衍生的风险线索在导航过程中调整安全裕度。所提出的框架通过一个基于信号包络和周期性的轻量级实时破碎机检测器来增强CBF。其输出作为外生风险，通过调制屏障函数直接在控制器中强制执行。该方法在仿真中通过两种CBF公式（圆形和目标对齐椭圆形）进行了评估，使用独轮车机器人在杂乱建筑环境中导航。结果表明，CBF安全滤波器在所有试验中均消除了安全违规，同时到达目标的比例分别为40.2%（圆形）与76.5%（椭圆形），因为椭圆形公式能更好地避免死锁。这种将音频感知集成到基于CBF的控制器中的方法，为在安全关键和动态环境中实现更丰富的多模态安全推理提供了一条途径。