VISION BASED OBSTACLE AVOIDANCE AND ODOMETERY FOR SWARMS OF SMALL SIZE ROBOTS

M. Shuja Ahmed, Reza Saatchi, Fabio Caparrelli

Abstract

In multi-robotic systems, an approach to the coordination of multiple robots with each other is called swarm robotics. In swarm robotic systems, small size robots with limited memory and processing resources are used. Integration of vision sensors in such robots can complicate the design of the robots but at the same time, a single vision sensor can be used for multiple objectives as it provide rich surrounding information. As the vision algorithms are normally computationally demanding and robots in swarm systems has limited memory and processing capabilities, so the requirements of light weight vision algorithms also arises. In this research, the use of vision sensor information is made for achieving multiple objectives. A solution to obstacle avoidance, which is the basic requirement as robots move in a cluttered environment and also odometry which is essential for robot localization, is provided using only visual clues. The approach developed in this research is computationally less expensive and suitable for small size robots, where processing and memory constraints limit the use of computationally expensive approaches. To achieve this a library of vision algorithms is developed and customized for Blackfin processor based robotic systems.

References

  1. Borenstein, J. and Koren, Y. (1985). A mobile platform for nursing robots. In IEEE Transactions on Industrial Electronics, Vol. 32, No. 2.
  2. Borenstein, J. and Koren, Y. (1988). Obstacle avoidance with ultrasonic sensors. In IEEE journal of robotics and automation, vol. ra-4, no. 2.
  3. Campbell, J., Sukthankar, R., Nourbakhsh, I., and Pahwa, A. (2005). A robust visual odometry and precipice detection system using consumer-grade monocular vision. In Proc. ICRA, Barcelona, Spain.
  4. Chao, M., Braunl, T., and Zaknich, A. (1999). Visuallyguided obstacle avoidance. In 6th International Conference on Neural Information Processing ICONIP.
  5. Harris, C. and Stephens, M. (1988). A combined corner and edge detector. In Proceedings of the 4th ALVEY vision conference, University of Manchester, England.
  6. Horn, Berthold, K. P., and Schunck, B. G. (1993). Determining optical flow. In Artificial Intelligence: 81-87.
  7. Howard, A. (2008). Real-time stereo visual odometry for autonomous ground vehicles. In IEEE/RSJ International Conference on Intelligent Robots and Systems.
  8. Kernbach, S., Scholz, O., Harada, K., Popesku, S., Liedke, J., Raja, H., Liu, W., Caparrelli, F., Jemai, J., Havlik, J., Meister, E., and Levi, P. (2010). Multi-robot organisms: State of the art. In ICRA 2010 Workshop Modular Robots: State of the Art.
  9. Kyprou, S. (2009). Simple but effective personal localisation using computer vision. In MEng Report. Department of Computing, Imperial College London.
  10. Lewis, J. P. (1995). Fast template matching. In Vision Interface, p. 120-123.
  11. Lucas, B. D. and Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. In IJCAI.
  12. Lukasiak, R., Katz, D., and Lukasiak, T. (2005). Enhance processor performance in open-source applications. In Analog Dialogue 39-02.
  13. Maimone, M., Cheng, Y., and Matthies, L. (2007). Two years of visual odometry on the mars exploration rovers. In Journal of Field Robotics. Special Issue: Special Issue on Space Robotics. Volume 24, Issue 3.
  14. Michels, J., Saxena, A., and Andrew, Y. (2005). High speed obstacle avoidance using monocular vision and reinforcement learning. In Proceedings of the 22nd International Conference on Machine Learning.
  15. Milford, M. and Wyeth, G. (2008). Mapping a suburb with a single camera using a biologically inspired slam system. In Robotics, IEEE Transactions on , vol.24, no.5, pp.1038-1053, Oct. 2008.
  16. Nistar, D., Naroditsky, O., and Bergen, J. (2006). Visual odometry for ground vehicle applications. In Journal of Field Robotics, Volume 23.
  17. Pomerleau, D. (1997). Visibility estimation from a moving vehicle using the ralph vision system. In In IEEE Conf. Intelligent Transportation Systems.
  18. Pratt, K. (2007). Smart sensors for optic flow, obstacle avoidance for mavs in urban environments. In International Journal of Advanced Robotic Systems.
  19. Schaerer, S. S. (2006). Practical visual odometry for small embedded systems. In Master's Thesis, Department of Electrical and Computer Engineering, University of Manitoba.
  20. Souhila, K. and Karim., A. (2007). Optical flow based robot obstacle avoidance. In International Journal of Advanced Robotic Systems.
  21. Younse, P. J. and Burks, T. F. (2007). Greenhouse robot navigation using klt feature tracking for visual odometry. In International Commission of Agricultural Engineering. CIGR E-Journal Volume 9.
Download


Paper Citation


in Harvard Style

Shuja Ahmed M., Saatchi R. and Caparrelli F. (2012). VISION BASED OBSTACLE AVOIDANCE AND ODOMETERY FOR SWARMS OF SMALL SIZE ROBOTS . In Proceedings of the 2nd International Conference on Pervasive Embedded Computing and Communication Systems - Volume 1: PECCS, ISBN 978-989-8565-00-6, pages 115-122. DOI: 10.5220/0003820701150122


in Bibtex Style

@conference{peccs12,
author={M. Shuja Ahmed and Reza Saatchi and Fabio Caparrelli},
title={VISION BASED OBSTACLE AVOIDANCE AND ODOMETERY FOR SWARMS OF SMALL SIZE ROBOTS},
booktitle={Proceedings of the 2nd International Conference on Pervasive Embedded Computing and Communication Systems - Volume 1: PECCS,},
year={2012},
pages={115-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003820701150122},
isbn={978-989-8565-00-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pervasive Embedded Computing and Communication Systems - Volume 1: PECCS,
TI - VISION BASED OBSTACLE AVOIDANCE AND ODOMETERY FOR SWARMS OF SMALL SIZE ROBOTS
SN - 978-989-8565-00-6
AU - Shuja Ahmed M.
AU - Saatchi R.
AU - Caparrelli F.
PY - 2012
SP - 115
EP - 122
DO - 10.5220/0003820701150122