A vision-based real-time obstacle avoidance’s rules utilising grid-edge-depth map

Budi Rahmani, Agus Harjoko, Tri Kuntoro Priyambodo

Abstract


This paper presents a new rules-based of a real-time decision system for an autonomous wheeled robot with the holonomic-drive system. The robot uses decisions to avoid collisions with obstacles. The decision rules based on Grid-Edge-Depth Map. The Grid-Edge-Depth map represents the obstacle’s position and distance in the environment. The generation process of the Grid-Edge-Depth map presented in previous research. The decisions of the first scenario with no destination point are forward, stop, 90o right turn, and 90o left turn. The decisions of the second, and third scenarios with a destination point are forward, stop, 90o right turn, 90o left turn, 45o forward to the right, 45o forward to the left, slide to the right, and slide to the left. The proposal tested in a 5x3 metre living environment. Finally, the experiment resulted in 93.3% of navigation’s success for all the scenarios.


Keywords


GED-map; Stereo vision; Robot; Navigation; Obstacle avoidance

References


S. Tijmons, G. C. H. E. de Croon, B. D. W. Remes, C. De Wagter, and M. Mulder, “Obstacle Avoidance Strategy using Onboard Stereo Vision on a Flapping Wing MAV,” IEEE Trans. Robot., vol. 33, no. 4, pp. 858–874, 2017.

B. Rahmani, A. Harjoko, T. K. . K. Priyambodo, and H. Aprilianto, “Research of Smart Real-time Robot Navigation System,” in AIP The 7th SEAMS-UGM Conference 2015, 2015, vol. 1707, pp. 1–8.

M. Papoutsidakis, “Intelligent Design and Algorithms to Control a Stereoscopic Camera on a Robotic Workspace Z-error Z-Error,” Int. J. Comput. Appl., vol. 167, no. 12, pp. 32–35, 2017.

K. N. Al-Mutib, E. A. Mattar, M. M. Alsulaiman, and H. Ramdane, “Stereo Vision SLAM Based Indoor Autonomous Mobile Robot Navigation,” 2014 IEEE Int. Conf. Robot. Biomimetics (ROBIO 2014), pp. 1584–1589, 2014.

L. Ran, Y. Zhang, Q. Zhang, and T. Yang, “Convolutional Neural Network-Based Robot Navigation Using Uncalibrated Spherical Images,” Sensors, vol. 17, no. 1341, pp. 1–18, 2017.

Y. Zhao, W. Cheng, L. Jia, and S. Ma, “The Obstacle Avoidance and Navigation Based on Stereo Vision for Mobile Robot,” 2010 Int. Conf. Optoelectron. Image Process., vol. 2, pp. 565–568, 2010.

S. Solak and E. D. Bolat, “Distance Estimation using Stereo Vision for Indoor Mobile Robot Applications,” IEEE - 9th Int. Conf. Electr. Electron. Eng. 2015, pp. 685–688, 2015.

R. A. Hamzah, H. N. Rosly, and S. Hamid, “An Obstacle Detection and Avoidance of A Mobile Robot With Stereo Vision Camera,” in 2011 International Conference on Electronic Devices, Systems and Applications (ICEDSA), 2011, no. 1, pp. 104–108.

J. Kim, C. Park, and I. S. Kweon, “Vision-based Navigation With Efficient Scene Recognition,” Intell. Serv. Robot., vol. 4, no. 3, pp. 191–202, 2011.

P. Benavidez, “Mobile Robot Navigation and Target Tracking System,” in 6th International Conference on System of Systems Engineering, 2011, pp. 299–304.

T. H. Nam, J. H. Shim, and Y. I. Cho, “A 2.5D map-based mobile robot localization via cooperation of aerial and ground robots,” Sensors (Switzerland), vol. 17, no. 12, pp. 1–24, 2017.

M. Chen, Z. Cai, and Y. Wang, “A method for mobile robot obstacle avoidance based on stereo vision,” IEEE Int. Conf. Ind. Informatics, no. 1, pp. 94–98, 2012.

J. Kim and Y. Do, “Moving Obstacle Avoidance of a Mobile Robot Using a Single Camera,” Procedia Eng., vol. 41, no. Iris, pp. 911–916, 2012.

Q. Zhang, D. Chen, and T. Chen, “An Obstacle Avoidance Method of Soccer Robot Based on Evolutionary Artificial Potential Field,” Energy Procedia, vol. 16, pp. 1792–1798, 2012.

F. A. Yaghmaie, A. Mobarhani, and H. D. Taghirad, “Study of Potential Ban Method for Mobile Robot Navigation in Dynamic Environment,” in Power Electronics, Drive Systems and Technologies Conference (PEDSTC), 2013 4th, 2013, pp. 535–540.

Y. Zhang, C. W. de Silva, D. Su, and Y. Xue, “Autonomous Robot Navigation with Self-learning for Collision Avoidance with Randomly Moving Obstacles,” in The 9th International Conference on Computer Science & Education (ICCSE 2014), 2014, pp. 117–122.

K. Sharma, “Improved visual SLAM: a novel approach to mapping and localization using visual landmarks in consecutive frames,” Multimed. Tools Appl., no. 301, pp. 1–22, 2017.

C. Brand, M. J. Schuster, H. Hirschm, and M. Suppa, “Stereo-Vision Based Obstacle Mapping for Indoor / Outdoor SLAM,” IEEE/RSJ Int. Conf. Intell. Robot. Syst., no. Iros, pp. 1846–1853, 2014.

S. Iizuka, T. Nakamura, and S. Suzuki, “Robot Navigation in Dynamic Environment Using Navigation Function APF with SLAM,” in 2014 10th France-Japan/ 8th Europe-Asia Congress on Mecatronics (MECATRONICS2014- Tokyo), 2014, pp. 89–92.

W. G. Aguilar, V. P. Casaliglla, and J. L. Pólit, “Obstacle Avoidance Based-Visual Navigation for Micro Aerial Vehicles,” Electronics, vol. 6, no. 10, pp. 1–23, 2017.

M. A. Martínez, J. L. Martínez, and J. Morales, “Motion Detection from Mobile Robots with Fuzzy Threshold Selection in Consecutive 2D Laser Scans,” Electronics, vol. 4, pp. 82–93, 2015.

S. Wangsiripitak, “In-building navigation system using a camera,” in 2014 6th International Conference on Knowledge and Smart Technology (KST), 2014, pp. 35–40.

T. Cao, Z. Y. Xiang, and J. L. Liu, “Perception in Disparity : An Efficient Navigation Framework for Autonomous Vehicles With Stereo Cameras,” IEEE Trans. Intell. Transp. Syst., vol. 16, no. 5, pp. 2935–2948, 2015.

B. Rahmani, A. Harjoko, and T. K. Priyambodo, “THE ACCURACY IMPROVEMENT OF OBJECT’S DISTANCE MEASUREMENT BASED ON GRID-EDGE-DEPTH MAP IN THE DETERMINATION OF WHEELED ROBOT’S DECISION OF DIRECTION,” Universitas Gadjah Mada, 2019.

B. O. Kennedy, “Stereo Camera Calibration,” University of Stellenbosch, 2002.

J.-Y. Bouquet, “Camera Calibration Toolbox for Matlab,” 2015. [Online]. Available: http://www.vision.caltech.edu/bouguetj/calib_doc/.

Y. Zhao and J. Lei, “A Camera Self-Calibration Method Based on Plane Lattice and Orthogonality,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 11, no. 4, pp. 767–774, 2013.

B. Rahmani, A. Harjoko, and T. K. Priyambodo, “Grid-edge-depth map building employing sad with sobel edge detector,” Int. J. Smart Sens. Intell. Syst., vol. 10, no. 3, pp. 551–566, 2017.

M. Khan, S. Hassan, S. I. Ahmed, and J. Iqbal, “Stereovision-based real-time obstacle detection scheme for Unmanned Ground Vehicle with steering wheel drive mechanism,” Proc. 2017 Int. Conf. Commun. Comput. Digit. Syst. C-CODE 2017, pp. 380–385, 2017.

J. Jin, Y. G. Kim, S. G. Wee, D. H. Lee, and N. Gans, “A Stable Switched-System Approach to Collision-Free Wheeled Mobile Robot Navigation,” J. Intell. Robot. Syst. Theory Appl., vol. 86, no. 3–4, pp. 599–616, 2017.

N. F. Mustamin, “Relative distance measurement between moving vehicles for manless driving,” in 2017 International Seminar on Application for Technology of Information and Communication: Empowering Technology for a Better Human Life, iSemantic 2017, 2017, pp. 38–41.




DOI: http://doi.org/10.11591/ijeecs.v19.i1.pp%25p
Total views : 12 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

shopify stats IJEECS visitor statistics