Real-Time Obstacle Avoidance Based on Floor Detection for Mobile Robots

Year 2020, , 845 - 853, 01.10.2020

Adem Hiçdurmaz , Adem Tuncer

https://doi.org/10.16984/saufenbilder.674122

Cited By: 2

Abstract

Obstacle detection and avoidance are two main problems that demand solutions in the autonomous movement of mobile robots. To this end, the robots have been equipped with sensors and cameras. This study proposes a new method that allows mobile robots to move freely without any collision in an uncertain (i.e., both static and dynamic) workspace by processing images taken using a real-time webcam. In the study, a robot was allowed to move depending on the visibility and suitability of the floor in the images. These steps were repeated for each new image and, furthermore, the images were segmented based on an adaptive threshold obtained by calculating the statistical parameters. This segmentation was aimed to separate the floor from other areas in the study. Experimental results demonstrate that the proposed method is extremely successful to separate the floor from other regions and has a low cost and flexible method for obstacle avoidance.

Keywords

mobile robot, obstacle avoidance, floor detection, adaptive threshold, image segmentation

References

• H. Surmann, A. Nüchter, and J. Hertzberg, “An autonomous mobile robot with a 3D laser range finder for 3D exploration and digitalization of indoor environments,” Robotics and Autonomous Systems, vol. 45, no. 3–4, pp. 181–198, 2003.
• S. Kim and H. Kim, “Optimally overlapped ultrasonic sensor ring design for minimal positional uncertainty in obstacle detection,” International Journal of Control, Automation and Systems, vol. 8, no. 6, pp. 1280–1287, 2010.
• A. Al-Kaff, F. García, D. Martín, A. De La Escalera, and J. M. Armingol, “Obstacle detection and avoidance system based on monocular camera and size expansion algorithm for UAVs,” Sensors, vol. 17, no. 5, pp. 1061, 2017.
• H. Alvarez, L. M. Paz, J. Sturm, and D. Cremers, “Collision avoidance for quadrotors with a monocular camera,” In Experimental Robotics, Springer, Cham., pp. 195–209, 2016.
• K. McGuire, G. De Croon, C. De Wagter, K. Tuyls, and H. Kappen, “Efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone,” IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 1070–1076, 2017.
• H. Zhang, D. E. Hernandez, Z. Su, and B. Su, “A low cost vision-based road-following system for mobile robots,” Applied Sciences, vol. 8, no. 9, pp. 1635, 2018.
• N. Otsu, “A threshold selection method from gray-level histograms,” IEEE transactions on systems, man, and cybernetics, vol. 9, no. 1, pp. 62–66, 1979.
• N. M. Zaitoun and M. J. Aqel, “Survey on image segmentation techniques,” Procedia Computer Science, vol. 65, pp. 797–806, 2015.
• N. Ikonomakis, K. N. Plataniotis, and A. N. Venetsanopoulos, “Color image segmentation for multimedia applications,” Journal of Intelligent and Robotic Systems, vol. 28, no. 1–2, pp. 5–20, 2000.
• H. Zhang, X. Wang, Y. Chen, G. Jiang, and S. Lin, “Research on Vision-Based Navigation for Plant Protection UAV under the Near Color Background,” Symmetry, vol. 11, no. 4, pp. 533, 2019.
• S. J. Dumble and P. W. Gibbens, “Horizon profile detection for attitude determination,” Journal of Intelligent & Robotic Systems, vol. 68, no. 3–4, pp. 339–357, 2012.
• W. Benn and S. Lauria, “Robot navigation control based on monocular images: an image processing algorithm for obstacle avoidance decisions,” Mathematical Problems in Engineering, 14 pages, 2012.
• S. A. Li, L. H. Chou, T. H. Chang, C. H. Yang, and Y. C. Chang, “Obstacle Avoidance of Mobile Robot Based on HyperOmni Vision,” Sensors and Materials, vol. 31, no. 3, pp. 1021–1036, 2019.
• V. S. Kalogeiton, K. Ioannidis, G. C. Sirakoulis, and E. B. Kosmatopoulos, “Real-Time Active SLAM and Obstacle Avoidance for an Autonomous Robot Based on Stereo Vision,” Cybernetics and Systems, vol. 50, no. 3, pp. 239–260, 2019.
• E. Dönmez, A. F. Kocamaz, and M. Dirik, “A Vision-Based Real-Time Mobile Robot Controller Design Based on Gaussian Function for Indoor Environment,” Arabian Journal for Science and Engineering, vol. 43, no. 12, pp. 7127–7142, 2018.
• A. Tuncer and M. Yildirim, “Design and implementation of a genetic algorithm IP core on an FPGA for path planning of mobile robots,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 24, no. 6, pp. 5055–5067, 2016.
• S. Mishra and M. Panda, “Bat algorithm for multilevel colour image segmentation using entropy-based thresholding,” Arabian Journal for Science and Engineering, vol. 43, no. 12, pp. 7285–7314, 2018.
• S. Bhowmick, A. Pant, J. Mukherjee, and A. K. Deb, “A novel floor segmentation algorithm for mobile robot navigation,” In 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), IEEE, pp. 1–4, 2015.
• C. Chun, D. Park, W. Kim, and C. Kim, “Floor detection based depth estimation from a single indoor scene,” In 2013 IEEE International Conference on Image Processing, pp. 3358–3362, 2013.
• Y. Li and S. T. Birchfield, “Image-based segmentation of indoor corridor floors for a mobile robot,” In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 837–843, 2010.
• M. Ling, W. Jianming, Z. Bo, and W. Shengbei, “Automatic floor segmentation for indoor robot navigation,” In 2nd International Conference on Signal Processing Systems, vol. 1, pp. 684–689, 2010.
• “About ROS.” [Online]. Available: https://www.ros.org/about-ros/. [Accessed: 10-Nov-2019].