The Tracking of a Moving Object by a Mobile Robot Following the Object’s Sound

Han, Jong Ho; Han, Sun-Sin; Lee, Jang-Myung

doi:10.1007/s10846-012-9769-3

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…In order to recognize the position of a sound source, it is possible to estimate the position of the sound source using TDOA (time difference of arrival) and phase difference method [22][23][24][25][26][27].…”

Section: Sound Source Localization Algorithmmentioning

confidence: 99%

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Tracking Control of Moving Sound Source Using Fuzzy-Gain Scheduling of PD Control

Han

2019

Electronics

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This paper proposes fuzzy gain scheduling of proportional differential control (FGS-PD) system for tracking mobile robot to moving sound sources. Given that the target positions of the real-time moving sound sources are dynamic, the mobile robots should be able to estimate the target points continuously. In such a case, the robots tend to slip owing to abnormal velocities and abrupt changes in the tracking path. The selection of an appropriate curvature along which the robot follows a sound source makes it possible to ensure that the robot reaches the target sound source precisely. For enabling the robot to recognize the sound sources in real time, three microphones are arranged in a straight formation. In addition, by applying the cross correlation algorithm to the time delay of arrival base, the received signals can be analyzed for estimating the relative positions and velocities of the mobile robot and the sound source. Even if the mobile robot is navigating along a curved path for tracking the sound source, there could be errors due to the inertial and centrifugal forces resulting from the motion of the mobile robot. Velocities of both robot wheels are controlled using FGS-PD control to compensate for slippage and to minimize tracking errors. For experimentally verifying the efficacy of the proposed the control system with sound source estimation, two mobile robots were fabricated. It was demonstrated that the proposed control method effectively reduces the tracking error of a mobile robot following a sound source.

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Section: Sound Source Localization Algorithmmentioning

confidence: 99%

“…For recognizing the locations of the sound sources, the cross-correlation algorithm is applied to the TDOA (time difference of arrival) base. Through this process, the signals are analyzed, and the locations of the sound sources are determined using the analyzed data values [19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Tracking Control of Moving Sound Source Using Fuzzy-Gain Scheduling of PD Control

Han

2019

Electronics

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“…Recently, various methodologies have been utilized for mobile robot navigation [1][2][3]. These are, artificial potential fields (APF) [4][5][6], vision-based gaze guided control (GGC) [7][8][9][10][11][12][13][14][15], fuzzy logic control [12,[16][17][18], vector field histogram (VFH) [19,20], rapidly-exploring random trees [21,22], and obstacle avoidance and path planning [23] algorithms. The fuzzy logic controller is one of the efficient techniques, which tends to be used actually for steering control in unpredictable environments [3,24,25].…”

Section: Introductionmentioning

confidence: 99%

Gaze-Guided Control of an Autonomous Mobile Robot Using Type-2 Fuzzy Logic

Dirik

Castillo

Kocamaz

2019

ASI

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Motion control of mobile robots in a cluttered environment with obstacles is an important problem. It is unsatisfactory to control a robot’s motion using traditional control algorithms in a complex environment in real time. Gaze tracking technology has brought an important perspective to this issue. Gaze guided driving a vehicle based on eye movements supply significant features of nature task to realization. This paper presents an intelligent vision-based gaze guided robot control (GGC) platform that uses a user-computer interface based on gaze tracking enables a user to control the motion of a mobile robot using eyes gaze coordinate as inputs to the system. In this paper, an overhead camera, eyes tracking device, a differential drive mobile robot, vision and interval type-2 fuzzy inference (IT2FIS) tools are utilized. The methodology incorporates two basic behaviors; map generation and go-to-goal behavior. Go-to-goal behavior based on an IT2FIS is more soft and steady progress in data processing with uncertainties to generate better performance. The algorithms are implemented in the indoor environment with the presence of obstacles. Experiments and simulation results indicated that intelligent vision-based gaze guided robot control (GGC) system can be successfully applied and the IT2FIS can successfully make operator intention, modulate speed and direction accordingly.

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“…However, the method is not robust against noises. The difference and cross correlation or the generalized cross correlation method have been used for robust similarity measurements [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

An Active Virtual Impedance Control Algorithm for Collision Free Navigation of a Mobile a Robot

Kim

Lee

2017

IJRA

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<span style="color: black; font-family: 'Times New Roman','serif'; font-size: 9pt; mso-fareast-font-family: '맑은 고딕'; mso-bidi-font-style: italic; mso-fareast-theme-font: minor-fareast; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">An modified active virtual impedance control has been proposed for collision free navigation of a mobile robot to avoid front obstacles dynamically while a mobile robot is following a sound source. A mobile robot is controlled to follow a sound source with a velocity which is determined by virtual repulsive and attraction forces to avoid obstacles and to follow the sound source, respectively. To generate the virtual repulsive and attraction forces, a new modified virtual impedance is defined as a function of the distances and relative velocities to the sound source and obstacles from the mobile robot. In the conventional virtual impedance method, fixed coefficients have been used for the virtual impedance control. In this research, the coefficients are dynamically adjusted to elaborate the obstacle avoidance performance in various situations such as the multiple moving obstacles environment. A microphone array consisting of three microphones in a row has been attached on the mobile robot to detect the relative distance and velocity to the obstacles. The relative position and orientation of the sound source against the mobile robot has been estimated using the geometrical relationship of the microphones. As an application, the mobile robot can be used as a pet robot following the master with a sound source. The effectiveness of the proposed algorithm has been demonstrated through real experiments.</span>

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The Tracking of a Moving Object by a Mobile Robot Following the Object’s Sound

Cited by 8 publications

References 15 publications

Tracking Control of Moving Sound Source Using Fuzzy-Gain Scheduling of PD Control

Tracking Control of Moving Sound Source Using Fuzzy-Gain Scheduling of PD Control

Gaze-Guided Control of an Autonomous Mobile Robot Using Type-2 Fuzzy Logic

An Active Virtual Impedance Control Algorithm for Collision Free Navigation of a Mobile a Robot

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