Experimental Evaluation of a Modified Obstacle Based Potential Field Algorithm for an Off-road Mobile Robot

Rickard Nyberg, George Nikolakopoulos, Dariusz Kominiak

2014

Abstract

This article presents an experimental evaluation of a modified obstacle based artificial potential field algorithm for an off-road mobile robot. The first contribution of the presented approach concerns the transformation of the artificial potential field method for the guidance of the vehicle and obstacle avoidance, in order to make it suitable for utilising a visual feedback. The visual feedback is relying on a depth image, provided by the low cost kinect sensor. The second contribution concerns the proposal of a novel scheme for the identification and perception of obstacles. Based on the proposed methodology, the vehicle is capable of categorising the obstacles based on their height in order to alter the calculated forces, for enabling a cognitive decision regarding their avoidance or the driving over them, by utilising the robot’s off road capabilities. The proposed scheme is highly suggested for off road robots, since in the normal cases, the existence of small rocks, branches, etc. can be accidentally identified as obstacles that could make the robot to avoid them or block its further movement. The performance of the proposed modified potential field algorithm has been experimentally applied and evaluated in multiple robotic exploration scenarios, where from the obtained results the efficiency and the advantages of such a modified scheme have been depicted.

References

  1. Broggi, A., Caraffi, C., Fedriga, R., and Grisleri, P. (2005). Obstacle detection with stereo vision for off-road vehicle navigation. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
  2. Choset, H., Lynch, K., S.Hutchinson, Kantor, G., Burgard, W., Kavraki, L., and Thrun, S. (2005). Principles of robot motion; theory, algorithms and implementations. MIT Press.
  3. Francis, C. J. (1983). Finding edges and lines in images. Massachusetts Inst. of Tech. Report 1.
  4. Guo, J., Gao, Y., and Guangzhao, C. (2013). Path planning of mobile robot based on improved potential field. Information Technology Journal, 12.
  5. Hirose, S., Fukushima, E., Damoto, R., and Nakamoto, H. (2001). Design of terrain adaptive versatile crawler vehicle helios-vi. International Conference on Intelligent Robots and Systems, pages 1540-1545.
  6. Justusson, B. (1981). Median filtering: Statistical properties. Springer.
  7. Kalmegh, S., Samra, D., and Rasegaonkar, N. (2010). Obstacle avoidance for a mobile exploration robot using a single ultrasonic range sensor. International Conference on Emerging Trends in Robotics and Communication Technologies, pages 8-11.
  8. Khatib, O. (1986). Real-time obstacle avoidance for manipulators and mobile robots. International Journal of Robotics Research, 5:500-505.
  9. Matthies, L., Gat, E., Harrison, R., Wilcox, B., Volpe, R., and Litwin, T. (1995). Mars microrover navigation: Performance evaluation and enhancement. Autonomous Robots, 2:291-311.
  10. Schlengel, N., Kachroo, P., Ball, J., and Bay, S. (1997). Image based control for scaled automated vehicles. IEEE Conference on Transportation System.
  11. Sercan, A. and Hakan, T. (2011). Robust motion control of a four wheel drive skid-steered mobile robot. 7th international conference on electrical and electronics engineering.
  12. Siegwart, R., Lamon, P., Estier, T., Lauria, M., and Piguet, R. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4):393-422.
  13. Vadakkepat, P., T., K., and Ming-Liang, W. (2000). Evolutionary artificial potential fields and their application in real time robot path planning. Proceedings of the 2000 Congress on IEEE Evolutionary Computation, 1.
  14. Valavanis, K., Hebert, T., Kolluru, R., and Tsouverloudis, N. (2000). Obstacle avoidance for a mobile exploration robot using a single ultrasonic range sensor. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 30:187-198.
  15. Zhang, T., Yi, Z., and Jingyan, S. (2010). Real-time motion planning for mobile robots by means of artificial potential field method in unknown environment. ndustrial Robot: An International Journal, pages 384-400.
Download


Paper Citation


in Harvard Style

Nyberg R., Nikolakopoulos G. and Kominiak D. (2014). Experimental Evaluation of a Modified Obstacle Based Potential Field Algorithm for an Off-road Mobile Robot . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-040-6, pages 626-633. DOI: 10.5220/0005121006260633


in Bibtex Style

@conference{icinco14,
author={Rickard Nyberg and George Nikolakopoulos and Dariusz Kominiak},
title={Experimental Evaluation of a Modified Obstacle Based Potential Field Algorithm for an Off-road Mobile Robot},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2014},
pages={626-633},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005121006260633},
isbn={978-989-758-040-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Experimental Evaluation of a Modified Obstacle Based Potential Field Algorithm for an Off-road Mobile Robot
SN - 978-989-758-040-6
AU - Nyberg R.
AU - Nikolakopoulos G.
AU - Kominiak D.
PY - 2014
SP - 626
EP - 633
DO - 10.5220/0005121006260633