具有音频风险意识的控制障碍函数用于建筑工地机器人安全导航
Control Barrier Functions with Audio Risk Awareness for Robot Safe Navigation on Construction Sites
AI总结：
本文提出一种基于控制障碍函数的机器人安全导航方法，通过音频风险提示增强避障能力，有效提升建设工地环境下的自主导航安全性。
摘要：
建设自动化日益需要自主移动机器人，但建设工地上的鲁棒自主性仍极具挑战性。这些环境是动态的，往往视觉上被遮挡，这使得感知和导航更加复杂。在此背景下，大多数自主性堆栈仍未充分利用音频源中的有价值信息。本文提出了一种基于控制障碍函数（CBF）的安全过滤器，该过滤器在避障时提供安全保证，并在导航过程中通过音频衍生的风险提示调整安全边距。所提出的框架增强了CBF，使其包含一个轻量级、实时的敲击声检测器，该检测器基于信号包络和周期性。其输出作为外部风险，直接在控制器中通过调节障碍函数来强制执行。该方法在模拟中进行了评估，使用了两种CBF形式（圆形和目标对齐的椭圆形）与一个在杂乱建设环境中导航的双轮机器人。结果显示，CBF安全过滤器在所有试验中消除了安全违规，同时到达目标的百分比为40.2%（圆形） vs. 76.5%（椭圆形），椭圆形形式更能避免死锁。将音频感知整合到基于CBF的控制器中展示了通往更丰富的多模态安全推理的路径，为安全关键和动态环境中的自主机器人提供安全保障。
英文摘要：
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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