HUMANOID ROBOT BASED ON STEREO VISION TO AVOID AN OBSTACLE

Authors

  • Warinthorn au Nualtim Department of Electronics Computer Technology Program, Faculty of Science and Technology, Bansomdetjchaopraya Rajabhat University, 1061 Isaraphab 15 Rd. Dhonburi, Bangkok 10600, Tel(02) 473700 Ext 3141 , 086-0685048

Keywords:

Humanoid robot, Stereo vision, Baseline, Disparity, Recognition

Abstract

In this research to improve humanoid robot was used to a single camera vision for the RoboCup soccer league competitions in Thailand. This research proposed the technique for humanoid robot avoids an obstacle based on a better vision system, called a stereo vision. To get more efficiency, two Logitech 905 model CCD cameras and microprocessor ATOM PICO 820 board can be used on humanoid robot to visualize the object and obstacle. The humanoid robot can recognize the object called tennis ball. The issue problem is mostly the robots bumping between competitions humanoid robot league due to the humanoid robots were used a camera vision system. Using two cameras for stereo vision can increase more angle of view than using a camera. Indeed, one of stereo vision frameworks is used to disparity between left and right cameras, and the relative baseline is fixed 5 centimeters. To measure the distance of the humanoid robot can measure well at 20 centimeters between the obstacle and the humanoid robot. As a result, humanoid robot to avoid obstacle was selected at a distance of 20 centimeters optimized.

References

Bradski, G. & Kaehler, A. (2008). Learning opencv. 1nd. USA: O’Reilly.

Bradsky, G. & Kaehler, A. (2008). Learning opencv. OReilly.sampling. IEEE Trans. Pattern Anal. Machine Intell. 20, 401-406

Eko, P., R. D. A., Nanik, S. & Chastine, F. (2017). Mango Leaf Image Segmentation on HSV and YCbCr Color Spaces Using Otsu Thresholding. International Conference on Science and Technology - Computer (ICST). 99 – 103.

Jain, R., Kasturi, R. & Schunck, B.G. (1995). MACHINE VISION. New York : McGRAW-Hill.

Jen, S. C., Chih, H. H. & Hung, W. H. (2013). A stereo vision-based self-localization system. IEEE Sensors Journal. 13(5), 1677–1689.

Jonathan, C. & Ary, S. P. (2016). Stereo visual odometry system design on humanoid robot nao. 6th International conference on System Engineering and Technology (ICSET). 34–38.

Maurice, F. F., Pat, M., Robin, D., Thomas, W., Matthaw, A. J. M. & Russ, T. (2015). Continuous humanoid locomotion over uneven terrain using stereo fusion. IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids).
881– 888.

Manaf, A. M., Amera, I. M. & Faris A. K. (2013). Object Distance Measurement by Stereo Vision. International Journal of Science and Applied.

M. I. Vargas-Signoret, M. Rojas-Romero, I. Trejo-vila, J. Velasco-Avella, E. E. Robles-Martnez, M. Santoyo-Mora, K. A. Camarillo-Gmez, G. I. Prez-Soto, & L. A.

Morales-Hernndez, (2016). Depth map construction with stereo vision for humanoid robot navigation. XVIII Congreso Mexicano de Robotica. 1–6.

Ningbro, H., Haibin L. Y. Q. & Jie, Y. (2016). Face Super-resolution Reconstruction and Recognition Using Non-local Similarity Dictionary Learning Based Algorithm. IEEE/CAA Journal of Automatica Sinica. 3(2), 213–224.

Nishad, P. & Dr. R.Manicka, C. (2013). VARIOUS COLOUR SPACES AND COLOUR SPACE CONVERSION ALGORITHMS. Journal of Global Research in Computer Science. 4(1), 44-48.

Romi, F. R., Tengku, C., Dani, G. & Opim. S. S. (2016). Skin color segmentation using multi-color space threshold. International Conference on Computer and Information Sciences (ICCOINS). 391–396.

Siviram, P. M. & Soma, B. (2016). Low resolution face recognition across variations in pose and illumination. IEEE Transactions on Pattern Analysis and Machine Intelligence. 38(5), 1034–1040.

Tsung-S, H. & Ta-C, W. (2013). An Improvement Stereo Vision Images Processing for Object Distance Measurement. National Science Council under Grant NSC-101-2221-E-006-143.

Wilman, W. W. Z. & Pong, C. Y. (2012). Very low resolution face recognition problem. IEEE Transactions on Image Processing. 21(1), 327–340.

Yasir, M. M., Rahizall, N., Hasbullah, H. & Amelia, W. A. (2012). Stereo vision images processing for real- time object distance and size measurements. International Conference on and Communication Engineering (ICCCE). 659-663.

Yuli, C., Yide, M., Ko-C, W., Dong, H. K. & Sung-K, P. (2015). Region-Based Object Recognition by Color Segmentation Using a Simplified PCNN hidden conditional random fields. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS. 26(8), 1682- 1697.

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Published

2018-04-20

Issue

Section

บทความวิจัย (Research Article)