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Sensors 2018, 18(4), 972; https://doi.org/10.3390/s18040972

Correction of Visual Perception Based on Neuro-Fuzzy Learning for the Humanoid Robot TEO

Systems Engineering and Automation Department, Universidad Carlos III de Madrid, Avd. Universidad, 30, Leganés, 28903 Madrid, Spain
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Received: 8 February 2018 / Revised: 19 March 2018 / Accepted: 23 March 2018 / Published: 25 March 2018
(This article belongs to the Section Physical Sensors)
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Abstract

New applications related to robotic manipulation or transportation tasks, with or without physical grasping, are continuously being developed. To perform these activities, the robot takes advantage of different kinds of perceptions. One of the key perceptions in robotics is vision. However, some problems related to image processing makes the application of visual information within robot control algorithms difficult. Camera-based systems have inherent errors that affect the quality and reliability of the information obtained. The need of correcting image distortion slows down image parameter computing, which decreases performance of control algorithms. In this paper, a new approach to correcting several sources of visual distortions on images in only one computing step is proposed. The goal of this system/algorithm is the computation of the tilt angle of an object transported by a robot, minimizing image inherent errors and increasing computing speed. After capturing the image, the computer system extracts the angle using a Fuzzy filter that corrects at the same time all possible distortions, obtaining the real angle in only one processing step. This filter has been developed by the means of Neuro-Fuzzy learning techniques, using datasets with information obtained from real experiments. In this way, the computing time has been decreased and the performance of the application has been improved. The resulting algorithm has been tried out experimentally in robot transportation tasks in the humanoid robot TEO (Task Environment Operator) from the University Carlos III of Madrid. View Full-Text
Keywords: humanoid robot; artificial vision; non-grasping manipulation; Neuro-Fuzzy filter; distortion correction humanoid robot; artificial vision; non-grasping manipulation; Neuro-Fuzzy filter; distortion correction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Hernandez-Vicen, J.; Martinez, S.; Garcia-Haro, J.M.; Balaguer, C. Correction of Visual Perception Based on Neuro-Fuzzy Learning for the Humanoid Robot TEO. Sensors 2018, 18, 972.

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