An Object Detection and Identification System for a Mobile Robot Control

Abstract

The one of the features of mobile robot control is to detect and to identify objects in workspace. Especially, autonomous systems must detect obstacles and then revise actual trajectories according to new conditions. Hence, many solutions and approaches can be found in literature. Different sensors and cameras are used to solve problem by many researchers. Different type sensors usage can affect not only system performance but also operational cost. In this study, single camera based obstacle detection and identification algorithm was developed to control omni-drive mobile robot systems. Objects and obstacles, which are in robot view, are detected and identified their coordinates by using developed algorithms dynamically. Developed algorithm was tested on Festo Robotino mobile robot. Proposed approach offers not only cost efficiency but also short process time. 

Keywords

• Mobile robot control,object detection,robotino;

References

1. [1] G.N. DeSouza, A.C. Kak, “Vision for Mobile Robot Navigation: A Survey”, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 24, No. 2, pp. 237-267, 2002.
2. [2] A. Kroll, S.Soldan, "Survey Results on Status, Needs and Perspectives for using Mobile Service Robots in Industrial Applications", 11th International Conference on Control, Automation, Robotics and Vision, pp. 621-626, December 7-10, 2010, Singapore.
3. [3] H. Takai, M. Miyake, K. Okuda, K. Tachibana,”A Simple Obstacle Arrangement Detection Algorithm for Indoor Mobile Robots”, 2nd International Asia Conference on Informatics in Control, Automation and Robotics, pp.110 – 113, Mar 6 - 7, 2010, Wuhan, China.
4. [4] J. Zhu, Y. Wang, H. Yu, Haixia Xu, Y. Shi, “Obstacle Detection and Recognition in Natural Terrain for Field Mobile Robot Navigation”, the 8th World Congress on Intelligent Control and Automation, pp. 6567 – 6572, July 6-9 2010, Jinan, China.
5. [5] T. Gandhi, M.T. Yang, R. Kasturi, O. I. Camps, L. D. Coraor, J. McCandless, “Performance Characterization of the Dynamic Programming Obstacle Detection Algorithm“, IEEE Transactions on Image Processing, Vol. 15, No. 5,pp.1202-1214 , MAY 2006.
6. [6] N. Morales, J.T. Toledo, L. Acosta, R. Arnay, “Real-Time Adaptive Obstacle Detection Based on Image Database”, Computer Vision and Image Understanding, Vol. 115, pp. 1273-1287, 2011.
7. [7] A.S. Karakaya, G. Küçükyıldız, H. Ocak, Z. Bingül, “Mobil Robot Platformu Üzerinde Engel Algılanması ve Optimal Yönün Belirlenmesi”, 20th Signal Processing and Communications Applications Conference (SIU), pp. 1-4, April 18-20, 2012, Mugla, Turkey.
8. [8] A.Talukder, R. Manduchi, A. Rankin, L. Matthies, “Fast and Reliable Obstacle Detection and Segmentation for Cross-country Navigation”, IEEE Intelligent Vehicle Symposium,Vol.2, pp. 610-618, June 17-21, 2002, Versailles, France.

9. [9] U. A. Khan, A. Fasih, K. Kyamakya, J. C. Chedjou, “Genetic Algorithm Based Template Optimization for a Vision System: Obstacle Detection”, XV International Symposium on Theoretical Engineering (ISTET), pp. 164-168, June 22-24, 2009, Lübeck, Germany.
10. [10] L. Liu, J. Cuib, J. Li “Obstacle Detection and Classification in Dynamical Background”, AASRI Conference on Computational Intelligence and Bioinformatics, pp. 435 – 440, July 1-2, 2012, Changsha, China.
11. [11] Z. Yankun, H. Chuyang, W., Norman, “A Single Camera Based Rear Obstacle Detection System”, IEEE Intelligent Vehicles Symposium (IV), pp.485-490, June 5-9, 2011, Baden-Baden, Germany.
12. [12] A.R. Derhgawen, D. Ghose, “Vision Based Obstacle Detection using 3D HSV Histograms”, Annual IEEE India Conference (INDICON),pp.1-4, December 16-18, 2011, Hyderabad, India.
13. [13] Y.C. Lin, C.T. Lin , W.C. Liu, L.T. Chen, “A Vision-Based Obstacle Detection System for Parking Assistance”, 8th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1627 – 1630, June 19-21, 2013, Melbourne, Australia.
14. [14] S. Das, I. Banerjee, T. Samanta, “Sensor Localization and Obstacle Boundary Detection Algorithm in WSN”, Third International Conference on Advances in Computing and Communications, pp. 412 – 415, August 29-31, 2013, Kochi, Kerala, India.
15. [15] S. E. Oltean, M. Dulau and R. Puskas, “Position Control of Robotino Mobile Robot Using Fuzzy Logic”, IEEE Int. Conf. on Automation Quality and Testing Robotics (AQTR), Cluj-Napoca, Romania, May 28 – 30, 2010.
16. [16] Festo Robotino Manual, 2010.
17. [17] http://www.openrobotino.org/ (Erişim Tarihi Ocak 2014).
18. [18] T. Lindeberg, "Detecting Salient Blob-Like Image Structures and Their Scales with a Scale-Space Primal Sketch: A Method for Focus-of-Attention", International Journal of Computer Vision Vol.11, No. 3,, pp 283–318, 1993.