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ROBOTIC SOUND SOURCE LOCALIZATION USING BIO-INSPIRED ACOUSTIC SENSORS

机译:利用生物启发声传感器实现机器人声源定位

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In sound source localization, there is a fundamental size limit; the smaller the size, the smaller the directional cues that are relied on to pinpoint the sound source. As such, it is challenging to develop miniature sound source localization robotic system where space is too confined to employ conventional microphone arrays without compromising localization performance. Our previous studies show that through mechanical coupling with well-tuned structural parameters, directional microphones inspired by the parasitic fly Ormia ochracea can amplify the minute interaural time delay (ITD) by more than ten times, which enables the reduction of device size significantly while maintaining localization performance. In this paper, Cramer Rao lower bound (CRLB) is derived for the fly-ear inspired sensor and the conventional directional microphones to study the effects of mechanical coupling on the decrease of the theoretical lower bound of azimuth estimation. This improvement gives mobile robots the capability to reactively localize sound in an indoor environment. Using this miniature sensor, new sound source localization method is proposed to localize a stationary sound source in 2-D (azimuth and elevation). In the proposed sound localization method, Model-Free Gradient Descent (MFGD) optimization method, one of the main challenges is to choose the appropriate cost function to achieve minimum number of iterations and the smallest absolute error. To this end, different cost functions are proposed and investigated with different control schemes. Simulation results showed the ability of this technique to solve the ambiguity problem and localize the sound source.
机译:在声源定位中,存在基本的大小限制。尺寸越小,用于精确定位声源的方向性提示越小。因此,开发微型声源定位机器人系统具有挑战性,在这种系统中,空间过于狭窄,无法采用传统的麦克风阵列而又不影响定位性能。我们之前的研究表明,通过机械耦合与结构参数进行良好调整,寄生蝇Ormia ochracea激发的定向传声器可以将微小的耳间时间延迟(ITD)放大十倍以上,从而可以在保持尺寸的同时显着减小设备尺寸本地化性能。本文以Cramer Rao下界(CRLB)为出发点,为飞耳式感应传感器和传统的定向传声器研究了机械耦合对减小方位角估计理论下界的影响。这项改进使移动机器人能够在室内环境中对声音进行反应性定位。使用这种微型传感器,提出了一种新的声源定位方法,可以将固定声源定位为二维(方位角和仰角)。在提出的声音定位方法,无模型梯度下降(MFGD)优化方法中,主要挑战之一是选择合适的代价函数以实现最小的迭代次数和最小的绝对误差。为此,提出了不同的成本函数,并采用了不同的控制方案进行了研究。仿真结果表明了该技术解决歧义问题和定位声源的能力。

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