Line-of-Sight Based 3D Localization of Parallel Kinematic Mechanisms

Zhou, Erkai; Zhu, Meiping; Hong, Alexander; Nejat, Goldie; Benhabib, B.

doi:10.21307/ijssis-2017-786

Cited by 1 publication

(2 citation statements)

References 36 publications

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“…The single node here is a fixed node, labelled as Σ, which compute the overall output as the summation of all incoming signal. It can be expressed as follows: 5 1 1 2 2…”

Section: Layer 5: (Summation Layer)mentioning

confidence: 99%

“…Huang [4] addressed a 3-D position control for a robot arm utilizing two-CCD vision geometry and inverse kinematics. Zhou et al [5] used position sensitive detector (PSD) for high-precision parallel kinematic mechanisms (PKMs) in order to allow them to accurately achieve their desired poses. Dallej et al [12] developed 3D pose visual servoing for cable driven parallel robots.…”

Section: Introductionmentioning

confidence: 99%

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ANFIS-Based Visual Pose Estimation of Uncertain Robotic Arm Using Two Uncalibrated Cameras

San

Win

Pyone

2018

WCMC

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This paper describes a new approach for the visual pose estimation of an uncertain robotic manipulator using ANFIS (Artificial Neuro-Fuzzy Inference System) and two uncalibrated cameras. The main emphasis of this work is on the ability to estimate the positioning accuracy and repeatability of a low-cost robotic arm with unknown parameters under uncalibrated vision system. The vision system is composed of two cameras; installed on the top and on the lateral side of the robot, respectively. These two cameras need no calibration; thus, they can be installed in any position and orientation with just the condition that the end-effector of the robot must remain always visible. A red-colored feature point is fixed on the end of the third robotic arm link. In this study, captured image data via two fixed-cameras vision system are used as the sensor feedback for the position tracking of an uncertain robotic arm. LabVolt R5150 manipulator in our laboratory is used as case study. The visual estimation system is trained using ANFIS with subtractive clustering method in MATLAB. In MATLAB, the robot, feature point and cameras are simulated as physical behaviors. To get the required data for ANFIS, the manipulator was maneuvered within its workspace using forward kinematics and the feature point image coordinates were acquired with the two cameras. Simulation experiments show that the location of the robotic arm can be trained in ANFIS using two uncalibrated cameras; and problems for computational complexity and calibration requirement of multi-view geometry can be eliminated. Observing Mean Square Error (MSE), Root Mean Square Error (RMSE), Error Mean and Standard Deviation Errors, the performance of the proposed approach is efficient for using as visual feedback in uncertain robotic manipulator. Further, the proposed approach using ANFIS and uncalibrated vision system has better in flexibility, user-friendly manner and computational concepts over conventional techniques.

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“…The single node here is a fixed node, labelled as Σ, which compute the overall output as the summation of all incoming signal. It can be expressed as follows: 5 1 1 2 2…”

Section: Layer 5: (Summation Layer)mentioning

confidence: 99%