Machine Vision-based Obstacle Avoidance for Mobile Robot

Nuryono Satya Widodo, Anggit Pamungkas

Abstract


Obstacle avoidance for mobile robots, especially humanoid robot, is an essential ability for the robot to perform in its environment. This ability based on the colour recognition capability of the barrier or obstacle and the field, as well as the ability to perform movements avoiding the barrier, detected when the robot detects an obstacle in its path. This research develops a detection system of barrier objects and a field with a colour range in HSV format and extracts the edges of barrier objects with the FindContoure method at a threshold filter value. The filter results are then processed using the Bounding Rect method so that the results are obtained from the object detection coordinate extraction. The test results detect the colour of the barrier object with OpenCV is 100%, the movement test uses the processing of the object's colour image and robot direction based on the contour area value> 12500 Pixels, the percentage of the robot making edging motion through the red barrier object is 80% and the contour area testing <12500 pixel is 70% of the movement of the robot forward approaching the barrier object.

Keywords


Obstacle Avoidance; FindContoure; HSV; Humanoid Robot; Image Processing

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References


E. Fosch-Villaronga, H. Felzmann, M. Ramos-Montero, and T. Mahler, “Cloud services for robotic nurses? Assessing legal and ethical issues in the use of cloud services for healthcare robots,” in IEEE International Conference on Intelligent Robots and Systems, 2018, pp. 290–296. DOI: 10.1109/IROS.2018.8593591

A. J. Huete, J. G. Victores, S. Martínez, A. Giménez, and C. Balaguer, “Personal autonomy rehabilitation in home environments by a portable assistive robot,” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., vol. 42, no. 4, pp. 561–570, 2012. DOI: 10.1109/TSMCC.2011.2159201

S. Sheng et al., “Design of an SSVEP-based BCI system with visual servo module for a service robot to execute multiple tasks,” Proc. - IEEE Int. Conf. Robot. Autom., pp. 2267–2272, 2017. DOI: 10.1109/ICRA.2017.7989260

H. M. Do, W. Sheng, and M. Liu, “An open platform of auditory perception for home service robots,” in IEEE International Conference on Intelligent Robots and Systems, 2015, vol. 2015-Decem, pp. 6161–6166. DOI: 10.1109/IROS.2015.7354255

B. Lee, J. Choi, C. Baek, and B. Zhang, “Robust Human Following by Deep Bayesian Trajectory Prediction for Home Service Robots,” 2018 IEEE Int. Conf. Robot. Autom., pp. 7189–7195, 2018. DOI: 10.1109/ICRA.2018.8462969

K. Kaneko et al., “Humanoid robot HRP-2Kai - Improvement of HRP-2 towards disaster response tasks,” IEEE-RAS Int. Conf. Humanoid Robot., vol. 2015-December, pp. 132–139, 2015. DOI: 10.1109/HUMANOIDS.2015.7363526

A. Romay et al., “Open source driving controller concept for humanoid robots: Teams hector and ViGIR at 2015 DARPA robotics challenge finals,” in 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), 2015, pp. 1147–1147. DOI: 10.1109/HUMANOIDS.2015.7363497

S. J. Yi and D. D. Lee, “Dynamic heel-strike toe-off walking controller for full-size modular humanoid robots,” IEEE-RAS Int. Conf. Humanoid Robot., pp. 395–400, 2016. DOI: 10.1109/HUMANOIDS.2016.7803306

V. Chernov, J. Alander, and V. Bochko, “Integer-based accurate conversion between RGB and HSV color spaces,” Comput. Electr. Eng., 2015. DOI: 10.1016/j.compeleceng.2015.08.005

Z. Liu, W. Chen, Y. Zou, and C. Hu, “Regions of interest extraction based on HSV color space,” in IEEE International Conference on Industrial Informatics (INDIN), 2012. DOI: 10.1109/INDIN.2012.6301214

D. S. Y. Kartika and D. Herumurti, “Koi fish classification based on HSV color space,” in Proceedings of 2016 International Conference on Information and Communication Technology and Systems, ICTS 2016, 2017, pp. 96–100. DOI: 10.1109/ICTS.2016.7910280

M. B. Asad, F. J. Sharna, N. Alam, and M. J. Islam, “HSV and template matching based Bengali road sign recognition technique,” in 2016 International Conference on Innovations in Science, Engineering and Technology, ICISET 2016, 2017. DOI: 10.1109/ICISET.2016.7856527

M. Salarian, A. Manavella, and R. Ansari, “A vision based system for traffic lights recognition,” in IntelliSys 2015 - Proceedings of 2015 SAI Intelligent Systems Conference, 2015, pp. 747–753. DOI: 10.1109/IntelliSys.2015.7361224

S. Saini, S. Nikhil, K. R. Konda, H. S. Bharadwaj, and N. Ganeshan, “An efficient vision-based traffic light detection and state recognition for autonomous vehicles,” in IEEE Intelligent Vehicles Symposium, Proceedings, 2017, no. Iv, pp. 606–611. DOI: 10.1109/IVS.2017.7995785

H. Waghmare, R. Kokare, and Y. Dandawate, “Detection and classification of diseases of Grape plant using opposite colour Local Binary Pattern feature and machine learning for automated Decision Support System,” in 3rd International Conference on Signal Processing and Integrated Networks, SPIN 2016, 2016, pp. 513–518. DOI: 10.1109/SPIN.2016.7566749

O. R. Indriani, E. J. Kusuma, C. A. Sari, E. H. Rachmawanto, and D. R. I. M. Setiadi, “Tomatoes classification using K-NN based on GLCM and HSV color space,” in Proceedings - 2017 International Conference on Innovative and Creative Information Technology: Computational Intelligence and IoT, ICITech 2017, 2018, vol. 2018-Janua, pp. 1–6. DOI: 10.1109/INNOCIT.2017.8319133

J. Gong, Y. Jiang, G. Xiong, C. Guan, G. Tao, and H. Chen, “The recognition and tracking of traffic lights based on color segmentation and CAMSHIFT for intelligent vehicles,” in IEEE Intelligent Vehicles Symposium, Proceedings, 2010, pp. 431–435. DOI: 10.1109/IVS.2010.5548083

N. S. Widodo, W. Arsadiando, A. Rahman, and M. I. F. Hatta, “Parameterized kick engine for R-SCUAD robot,” in International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2017. DOI: 10.1109/EECSI.2017.8239167

V. K. Bohat and K. V. Arya, “A new heuristic for multilevel thresholding of images,” Expert Syst. Appl., 2019. DOI: 10.1016/j.eswa.2018.08.045




DOI: http://dx.doi.org/10.26555/jiteki.v5i2.14767

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Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)
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