用麦克风阵列实现机器人触碰定位，精度达0.42cm。
SonicBoom: Contact Localization Using Array of Microphones
- 基于麦克风阵列与学习模型，通过声信号定位触碰位置。
- 在真实交互中定位误差仅0.42cm，新物体下仍保持2.22cm精度。
- 适合视觉受阻场景下的机器人导航与触觉感知任务。
在农业等复杂环境中，视觉传感器常因遮挡失效，触觉信号可提供关键空间信息以帮助机器人定位刚性物体并避障。我们提出SonicBoom，一种集成硬件与学习的完整系统，利用麦克风阵列实现接触定位。传统声源定位方法适用于空气介质，但在不规则固体介质中的定位难以解析建模。为此，我们采用特征工程与学习相结合的方法，自主采集18,000组机器人交互声学数据对，学习声信号与末端执行器接触位置之间的映射关系。通过分析麦克风间的相对特征，SonicBoom在分布内交互中实现0.42cm的定位误差，并在新物体和新接触条件下仍保持2.22cm的鲁棒性能。我们在模拟树冠环境下实现了对遮挡枝条的触觉测绘，验证了声学感知在视觉挑战环境中的可靠导航能力。
原文摘要 · Abstract (English)
In cluttered environments where visual sensors encounter heavy occlusion, such as in agricultural settings, tactile signals can provide crucial spatial information for the robot to locate rigid objects and maneuver around them. We introduce SonicBoom, a holistic hardware and learning pipeline that enables contact localization through an array of contact microphones. While conventional sound source localization methods effectively triangulate sources in air, localization through solid media with irregular geometry and structure presents challenges that are difficult to model analytically. We address this challenge through a feature engineering and learning based approach, autonomously collecting 18,000 robot interaction sound pairs to learn a mapping between acoustic signals and collision locations on the robot end effector link. By leveraging relative features between microphones, SonicBoom achieves localization errors of 0.42cm for in distribution interactions and maintains robust performance of 2.22cm error even with novel objects and contact conditions. We demonstrate the system's practical utility through haptic mapping of occluded branches in mock canopy settings, showing that acoustic based sensing can enable reliable robot navigation in visually challenging environments.
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