A Sound Source Tracking System Based on Robot Hearing and Vision

Lv, Xinyi; Zhang, Minglu; Liu, Xuan

doi:10.1109/csse.2008.766

Cited by 3 publications

(2 citation statements)

References 5 publications

(5 reference statements)

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“…X.L. Lv [2] analyzed the possible error source of positioning, and proposed the error equations of the vision system based on the liner model. J. Lu [3] proposed the analysis and simulation method of synthetical error for the harvesting robot in the complex environment both from the vision system and the robot mechanism.…”

Section: Introductionmentioning

confidence: 99%

The Correlative Positioning Error Compensation of the Vision System and the Robot Mechanism Based on BP Neural Network

Zou

Xiong

et al. 2014

KEM

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Error analysis and compensation of positioning is one of the basic problems for robot control. There are some limitations to study the error compensation either from the perspective of vision system or mechanism of the robot. Therefore, this paper studied the error compensation from both aspects with BP neural network model. Firstly, aiming at the depth values obtained by the vision system, a depth error database was established and based on BP neural network the depth error was effectively compensated within 5mm in MATLAB simulation. Secondly, aiming at the mechanism of a robot prototype the error compensation simulation based on BP neural network in MATLAB was carried out, and the simulation result showed that the error was within ±1mm after compensation. Thirdly, the correlative positioning error was defined and its error compensation simulation was carried out in MATLAB with the BP neural networks of the vision system and the mechanism; the simulation results showed that the correlative positioning error was approximately zero in this study. Finally, the error compensation of the robot mechanism based on the BP neural network was carried out on the robot prototype HNrobot2, and the experiment results showed that the positioning error was by 60% less, which was within 5mm.

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Section: Introductionmentioning

confidence: 99%

The Correlative Positioning Error Compensation of the Vision System and the Robot Mechanism Based on BP Neural Network

Zou

Xiong

et al. 2014

KEM

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“…4 Because different sensor fusion may provide effective recognition of environment, combinatorial use of auditory and visual sensors for sound localization is also studied. [5][6][7] Detection of a threedimensional position of sound source by two microphones * Correspending author. E-mail: uchiyama@mech.tut.ac.jp like the human's ear is also a topic in this area.…”

Section: Introductionmentioning

confidence: 99%

Sound source tracking considering obstacle avoidance for a mobile robot

2010

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SUMMARYSound source tracking is an important function for autonomous robots, because sound is omni-directional and can be recognized in dark environment. This paper presents a new approach to sound source tracking for mobile robots using auditory sensors. We consider a general type of two-wheeled mobile robot that has wide industrial applications. Because obstacle avoidance is also an indispensable function for autonomous mobile robots, the robot is equipped with distance sensors to detect obstacles in real time. To deal with the robot's nonholonomic constraint and combine information from the auditory and distance sensors, we propose a model reference control approach in which the robot follows a desired trajectory generated by a reference model. The effectiveness of the proposed method is confirmed by experiments in which the robot is expected to approach a sound source while avoiding obstacles.

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