基于声达时间差的移动机器人声源目标定位方法研究
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摘要
伴随着移动机器人应用领域的日益扩大，其所面对的工作环境也愈来愈复杂。面对呈现多种信息的各种不确定和非结构化的环境，单一的视觉感知方式必然降低机器人对环境的适应性，进而影响其智能决策水平。作为机器人处理声音信息的器官，从仿人角度出发而构建的机器人听觉系统，已成为机器人领域的重要研究对象。其利用声音传播的物理特性，可以与机器人视觉系统相配合，弥补其视角有限、依赖光照及无法穿过非透光障碍物等局限性。本文针对目标定位这一环境感知领域的基本问题，通过将移动机器人技术与声源定位技术进行有机结合，旨在实现噪声条件下基于声达时间差的移动机器人声源目标定位。本文围绕这一研究目标主要完成了以下四方面研究工作：
（1）在研究基于麦克风阵列的声源定位方法的基础上，以实现在三维空间内准确定位有声目标为目的，考虑系统实时性，开发制作了正四面体麦克风阵列，并通过数据采集器将其所采集的声音信息传递给机器人，设计实现了完整的机器人听觉系统。
（2）针对在实际工作条件下存在大量复杂噪声的问题，以提高抗噪声和抗混响性能为目标，研究了广义互相关和自适应滤波两种基本声达时间差估计方法。并通过仿真实验对代表性方法进行性能分析，总结了各算法的适用场合和优缺点，进而对广义互相关与自适应滤波相结合的声达时间差估计方法进行了研究。
（3）根据正四面体阵列结构和声音球面波传播模型，建立精确的几何定位计算模型，在此基础上推导出基于远场假设的声源定位计算模型。通过仿真性能分析，提出了一种结合机器人主动运动的全范围声源定位方法。同时，利用神经网络的非线性拟合能力和并行计算的特点，提出了一种基于BP神经网络的声源定位计算模型。
（4）针对传感器观测的不确定性和突发性强干扰导致的大量野值存在的问题，考虑移动机器人与声源目标产生相对运动的情况，提出了一种基于新息抗野值的二次卡尔曼滤波方法。该方法可根据机器人的运动状态，通过调整观测噪声协方差的估计量，提高了机器人在进行连续目标定位时的声源定位系统可靠性。
Along with the increasing expansion of application fields, mobile robots are facingincreasingly complex work environments. In all uncertain and unstructured environments whicha variety of information presented, mobile robots only rely on visual means to obtain information,is bound to reduce the environmental adaptability of the mobile robot, and thus limit their levelof intelligent decision. As the robot’s organ for processing sound information, the robot auditorysystem which is built from the idea of humanoid, has become an important research object inrobotics field. By using the physical characteristics of sound propagation, robot auditory systemcan be compatible with the visual system to compensate for its limitations, such as theperspective is limited, dependent on light and can’t pass through the non-translucent obstacles.Through researching target localization which is a basic problem in the field of environmentalperception, this thesis aims to realize mobile robot’s sound source localization based on timedelay of arrival in noise conditions by organically combining mobile robot technology withsound source localization technology. So, the following four research works have been done inthis thesis.
(1) Based on the study of sound source localization method which is using microphonearray, in order to achieve accurate positioning for the sound target in3D space, and consideringthe system’s real-time, a tetrahedral microphone array is developed and produced, and then itsobtained sound information pass to mobile robot through data acquisition system, so a completerobot auditory system has been realized.
(2) To solve the problem that there are many noises under actual working conditions, andimprove the performance of anti-noise and reverberation, two basic estimation methods for time delay of arrival, generalized cross correlation estimation and adaptive filtering, are studied. Andthe paper analyzs the performance of typical methods through simulation experiments,summarizs the application occasions, the advantages and disadvantages of the typical algorithms.Then the time delay of arrival estimation method based on the combination of generalized crosscorrelation and adaptive filter is studied.
(3) According to the tetrahedral microphone array structure and sound propagation sphericalwave model, precise geometric localization calculation model has been established. On this basis,the far-field sound source localization calculation model can be derived. After performancesimulation analysis, a full-ranged sound source orientation method combined with robot’s activemovement has been presented. And by using neural network’s capability of nonlinear curvefitting and parallel computing, a sound source orientation calculation model based on BP neuralnetwork has been also presented.
(4) To solve the problem which a large number of outliers exist caused by sensorobservation uncertainty and sudden strong interference, considering the relative motion betweenmobile robot and sound source, an anti-outliers double Kalman filtering method based oninnovation has been presented. Experimental results show that this method can improve system’sreliability by adjusting measurement noise covariance during continuous sound source targetlocalization and tracking.
（1）在研究基于麦克风阵列的声源定位方法的基础上，以实现在三维空间内准确定位有声目标为目的，考虑系统实时性，开发制作了正四面体麦克风阵列，并通过数据采集器将其所采集的声音信息传递给机器人，设计实现了完整的机器人听觉系统。
（2）针对在实际工作条件下存在大量复杂噪声的问题，以提高抗噪声和抗混响性能为目标，研究了广义互相关和自适应滤波两种基本声达时间差估计方法。并通过仿真实验对代表性方法进行性能分析，总结了各算法的适用场合和优缺点，进而对广义互相关与自适应滤波相结合的声达时间差估计方法进行了研究。
（3）根据正四面体阵列结构和声音球面波传播模型，建立精确的几何定位计算模型，在此基础上推导出基于远场假设的声源定位计算模型。通过仿真性能分析，提出了一种结合机器人主动运动的全范围声源定位方法。同时，利用神经网络的非线性拟合能力和并行计算的特点，提出了一种基于BP神经网络的声源定位计算模型。
（4）针对传感器观测的不确定性和突发性强干扰导致的大量野值存在的问题，考虑移动机器人与声源目标产生相对运动的情况，提出了一种基于新息抗野值的二次卡尔曼滤波方法。该方法可根据机器人的运动状态，通过调整观测噪声协方差的估计量，提高了机器人在进行连续目标定位时的声源定位系统可靠性。
Along with the increasing expansion of application fields, mobile robots are facingincreasingly complex work environments. In all uncertain and unstructured environments whicha variety of information presented, mobile robots only rely on visual means to obtain information,is bound to reduce the environmental adaptability of the mobile robot, and thus limit their levelof intelligent decision. As the robot’s organ for processing sound information, the robot auditorysystem which is built from the idea of humanoid, has become an important research object inrobotics field. By using the physical characteristics of sound propagation, robot auditory systemcan be compatible with the visual system to compensate for its limitations, such as theperspective is limited, dependent on light and can’t pass through the non-translucent obstacles.Through researching target localization which is a basic problem in the field of environmentalperception, this thesis aims to realize mobile robot’s sound source localization based on timedelay of arrival in noise conditions by organically combining mobile robot technology withsound source localization technology. So, the following four research works have been done inthis thesis.
(1) Based on the study of sound source localization method which is using microphonearray, in order to achieve accurate positioning for the sound target in3D space, and consideringthe system’s real-time, a tetrahedral microphone array is developed and produced, and then itsobtained sound information pass to mobile robot through data acquisition system, so a completerobot auditory system has been realized.
(2) To solve the problem that there are many noises under actual working conditions, andimprove the performance of anti-noise and reverberation, two basic estimation methods for time delay of arrival, generalized cross correlation estimation and adaptive filtering, are studied. Andthe paper analyzs the performance of typical methods through simulation experiments,summarizs the application occasions, the advantages and disadvantages of the typical algorithms.Then the time delay of arrival estimation method based on the combination of generalized crosscorrelation and adaptive filter is studied.
(3) According to the tetrahedral microphone array structure and sound propagation sphericalwave model, precise geometric localization calculation model has been established. On this basis,the far-field sound source localization calculation model can be derived. After performancesimulation analysis, a full-ranged sound source orientation method combined with robot’s activemovement has been presented. And by using neural network’s capability of nonlinear curvefitting and parallel computing, a sound source orientation calculation model based on BP neuralnetwork has been also presented.
(4) To solve the problem which a large number of outliers exist caused by sensorobservation uncertainty and sudden strong interference, considering the relative motion betweenmobile robot and sound source, an anti-outliers double Kalman filtering method based oninnovation has been presented. Experimental results show that this method can improve system’sreliability by adjusting measurement noise covariance during continuous sound source targetlocalization and tracking.
引文
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