Mobile Robot Navigation Techniques Using Potential Field Method in Unknown Environments

Abstract:

In this paper, a navigation system for autonomous mobile robots that move into unknown environments based on artificial potential field is presented. The robot moves to a predefined target point while detects and maps every encounter object using its artificial monocular vision system based on intrinsic camera parameters. During navigation, every obstacle is associated with a repulsive field depending on the distance and relative position to the robot, while the target point has an attractive field. Combining those values of potential field defines the direction of the next step of the robot. The results reported are showing the behavior of the method in three scenarios, avoiding obstacles, going through a narrow corridor and escaping from a minimum local trap.

• Periodical: Applied Mechanics and Materials (Volume 656)
• Pages: 388-394
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.656.388
• Online since: October 2014
• Authors: Stefan A. Dumitru, Luige Vladareanu*, Tian Hong Yan, Chen Kun Qi
• Keywords: Inverse Perspective Transformation, Potential Field, Robot Navigation

References

[1] R. Palm and A. Bouguerra, Navigation of mobile robots by potential field methods and market-based optimization. ECMR 2011, Oerebro, Sweden, (2011).

[2] Xiao Sun, Xiaoguang Zhao, En Li, Hongchun Li, Zize Liang, Mobile Robot Navigation Using RSSI Potential Field in Wireless Sensor Network", Journal of Computational Information Systems 6: 14 (2010) 4751-4759.

[3] R. Arunkumar, N. Balaji, Mobile robot navigation using potential field algorithm, International Journal of Advanced Engineering Applications, Vol. 4, Iss. 2, pp.26-32, (2011).

[4] Naoya Ohnishi, Atsushi Imiya, Navigation of Nonholonomic Mobile Robot Using Visual Potential Field, Proceedings of the 5th International Conference on Computer Vision Systems (ICVS 2007).

DOI: 10.1109/crv.2007.21

[5] Paolo Burelli, ArnavJhala, Dynamic Artificial Potential Fields for Autonomous Camera Control, Artificial Intelligence and Interactive Digital Entertainment, (2009).

DOI: 10.1609/aiide.v5i1.12363

[6] Luciano C. A. Pimenta, Alexandre R. Fonseca, Guilherme A. S. Pereira, Renato C. Mesquita, Elson J. Silva, Walmir M. Caminhas, and Mario F. M. Campos, Robot Navigation Based on Electrostatic Field Computation, IEEE TRANSACTIONS ON MAGNETICS, VOL. 42, NO. 4, APRIL (2006).

DOI: 10.1109/tmag.2006.870931

[7] Victor Vladareanu, Gabriela Tont, LuigeVladareanu, FlorentinSmarandache, The navigation of mobile robots in non-stationary and non-structured environments, Inderscience Publishers, Int. J. Advanced Mechatronic Systems, Vol. 5, No. 4, 2013, pg. 232- 243, ISSN online: 1756-8420, ISSN print: 1756-8412.

DOI: 10.1504/ijamechs.2013.057663

[8] Vladareanu Victor, Mingcong Deng, Schiopu Paul, Robots Extension Control using Fuzzy Smoothing, Proceedings of the 2013 International Conference on Advanced Mechatronic Systems, Luoyang, China, September 25-27, 2013, pg. 511-516, ISBN 978-0-9555293-9-9.

DOI: 10.1109/icamechs.2013.6681698

[9] Victor Vladareanu, OvidiuIlieSandru, LuigeVladareanu, Hongnian Yu, Extension Dynamical Stability Control Strategy for the Walking Robots, International Journal of Technology Management, SKIMA 2013, Inderscience Publisher, ISSN online: 1741-5276, ISSN.

[10] Francisco Bonin-Font, Alberto Ortiz and Gabriel Oliver (2010). A Visual Navigation Strategy Based on Inverse Perspective Transformation, Robot Vision, Ales Ude (Ed. ), ISBN: 978-953-307-077-3, InTech, DOI: 10. 5772/9309.

DOI: 10.5772/9309

[11] Jonathan S. Gardner, Joseph L. Austerweil, And Stephen E. Palmer, Vertical position as a cue to pictorial depth: Height in the picture plane versus distance to the horizon, Attention, Perception, & Psychophysics, 2010, 72 (2), 445-453, doi: 10. 3758/APP. 72. 2. 445.

DOI: 10.3758/app.72.2.445

[12] S. Tuohy, D. O'Cualain, E. Jones, M. Glavin, Distance Determination for an Automobile Environment using Inverse Perspective Mapping in OpenCV, IET Irish Signals and Systems Conference. IET, 2010, p.100–105.

DOI: 10.1049/cp.2010.0495

[13] Chin-TengLin, Tzu-KueiShen, Yu-Wen Shou, Construction of Fisheye Lens Inverse Perspective Mapping Model and Its Applications of Obstacle Detection, EURASIP Journal on Advances in Signal Processing, Volume 2010, doi: 10. 1155/2010/296598.

DOI: 10.1155/2010/296598

[14] AshutoshSaxena, Min Sun, Andrew Y. Ng, Make3D: Depth Perception from a Single Still Image, Proceedings of the 23rd national conference on Artificial intelligence - Volume 3, AAAI Press, 1571–1576.

[15] R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, p.175, (2000).

[16] Z. Sun, On-road vehicle detection, in IEEE Transactions on Pattern Analysis and Machine Intelligence, p.694–711, (2006).

[17] Gregory Dudek and Michael Jenkin, Computational principles of mobile robotics, Cambridge University Press, Cambridge, 2000, ISBN: 0-521-56021-7.

[18] Qing Li, Lijun Wang ; Bo Chen ; Zhou Zhou, An improved artificial potential field method for solving local minimum problem, Intelligent Control and Information Processing (Volume: 1 ), p.420 – 424, ), (2011).

DOI: 10.1109/icicip.2011.6008278

[19] Jian Yang, Jingrong Yu, Mi Dong, JiangminChunyu, A Strategy to Solve the Local Minima Problem for Autonomous Mechanical Vehicles, Advances in Mechanical and Electronic Engineering, LNEE 176, pp.605-610, Springer, (2012).

DOI: 10.1007/978-3-642-31507-7_96

[20] Manish Malakar, NishantSinha, A Particle Swarm Optimized Potential Field Method for Obstacle Avoidance and Path Planning of Mobile Robot, International Journal of Digital Application & Contemporary research, Volume 2, Issue 5, (2013).

[21] Steven Byrne, WasifNaeem, Stuart Ferguson, Improved APF strategies for dual-arm local motion planning, Transactions of the Institute of Measurement and Control, 2014, DOI: 10. 1177/0142331214532002.

DOI: 10.1177/0142331214532002

[22] Yan-Ping Zhang, Qing-GeJi, Ka-Ho Li, Ming-Min Zhang, Resolving Local Minima Problem of Potential Field, VRCAI '10 Proceedings of the 9th ACM SIGGRAPH Conference on Virtual-Reality Continuum and its Applications in Industry, Pages 339-346, (2010).

DOI: 10.1145/1900179.1900250