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Journal of Robotics
Volume 2017, Article ID 8796531, 11 pages
Research Article

A Global Path Planning Algorithm Based on Bidirectional SVGA

1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2School of Information Technology, Jiangsu Maritime Institute, Nanjing 211170, China

Correspondence should be addressed to Taizhi Lv; moc.361@ihziatvl

Received 3 August 2016; Revised 29 November 2016; Accepted 4 January 2017; Published 2 February 2017

Academic Editor: Yuan F. Zheng

Copyright © 2017 Taizhi Lv et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. Algabri, H. Mathkour, H. Ramdane, and M. Alsulaiman, “Comparative study of soft computing techniques for mobile robot navigation in an unknown environment,” Computers in Human Behavior, vol. 50, pp. 42–56, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. P. Raja and S. Pugazhenthi, “Optimal path planning of mobile robots: a review,” International Journal of Physical Sciences, vol. 7, no. 9, pp. 1314–1320, 2012. View at Google Scholar
  3. O. Montiel, R. Sepúlveda, and U. Orozco-Rosas, “Optimal path planning generation for mobile robots using parallel evolutionary artificial potential field,” Journal of Intelligent and Robotic Systems: Theory and Applications, vol. 79, no. 2, pp. 237–257, 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. K. R. Guruprasad, “EgressBug: a real time path planning algorithm for a mobile robot in an unknown environment,” in Proceedings of the International Conference on Advanced Computing, Network and Security, pp. 228–236, Surathkal, India, 2011.
  5. T. Li, S. Sun, and Y. Gao, “Fan-shaped grid based global path planning for mobile robot,” Robot, vol. 32, no. 4, pp. 547–552, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. L. J. Guo, W. X. Shi, Y. Li, and F. X. Li, “Mapping algorithm using adaptive size of occupancy grids based on quadtree,” Control and Decision, vol. 26, no. 11, pp. 1690–1694, 2011. View at Google Scholar · View at MathSciNet
  7. M. Shao and K. Shin, “Sensor-based path planning for planar two-identical-link robots by generalized voronoi graph,” Journal of the Korea Academia-Industrial Cooperation Society, vol. 15, no. 12, pp. 6986–6992, 2014. View at Publisher · View at Google Scholar
  8. Y. Gigras and K. Gupta, “Artificial intelligence in robot path planning,” International Journal of Soft Computing & Engineering, vol. 2, no. 2, pp. 471–474, 2012. View at Google Scholar
  9. D. Q. Zhu and M. Z. Yan, “Survey on technology of mobile robot path planning,” Control and Decision, vol. 25, no. 7, pp. 961–967, 2010. View at Google Scholar · View at Scopus
  10. V. T. Huynh, M. Dunbabin, and R. N. Smith, “Convergence-guaranteed time-varying RRT path planning for profiling floats in 4-Dimensional flow,” in Proceedings of the Australian Conference on Robotics and Automation, pp. 1–10, Melbourne, Australia, 2014.
  11. H.-P. Huang and S.-Y. Chung, “Dynamic visibility graph for path planning,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '04), pp. 2813–2818, IEEE, Sendai, Japan, October 2004. View at Scopus
  12. N. Tran, D.-T. Nguyen, D.-L. Vu, and N.-V. Truong, “Global path planning for autonomous robots using modified visibility-graph,” in Proceedings of the 2nd International Conference on Control, Automation and Information Sciences (ICCAIS '13), pp. 317–321, NhaTrang, Vietnam, November 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. Q. Zhang, J.-C. Ma, and L.-Y. Ma, “Environment modeling approach based on simplified visibility graph,” Journal of Northeastern University, vol. 34, no. 10, pp. 1383–1391, 2013. View at Google Scholar · View at Scopus
  14. L. I. Ping, J. Y. Zhu, F. Peng, and L. Yang, “Path planning based on visibility graph and A algorithm,” Computer Engineering, vol. 40, no. 3, pp. 193–195, 2014. View at Google Scholar
  15. T. T. N. Nguyet, T. V. Hoai, and N. A. Thi, “Some advanced techniques in reducing time for path planning based on visibility graph,” in Proceedings of the 3rd International Conference on Knowledge and Systems Engineering (KSE '11), pp. 190–194, Hanoi, Vietnam, October 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Kim, M. Kim, and D. Kim, “Variants of the quantized visibility graph for efficient path planning,” Advanced Robotics, vol. 25, no. 18, pp. 2341–2360, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Huang and Y. Cen, “A path-planning algorithm for AGV based on the combination between ant colony algorithm and immune regulation,” Advanced Materials Research, vol. 422, pp. 3–9, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. F. Duchoň, A. Babinec, M. Kajan et al., “Path planning with modified a star algorithm for a mobile robot,” Procedia Engineering, vol. 96, pp. 59–69, 2014. View at Publisher · View at Google Scholar
  19. S. M. Persson and I. Sharf, “Sampling-based A algorithm for robot path-planning,” International Journal of Robotics Research, vol. 33, no. 13, pp. 1683–1708, 2014. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Phillips, M. Likhachev, and S. Koenig, “PA∗SE: parallel A∗ for slow expansions,” in Proceedings of the 24th International Conference on Automated Planning and Scheduling (ICAPS '14), pp. 208–216, Portsmouth, NH, USA, June 2014. View at Scopus
  21. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Pearson Prentice Hall, Upper Saddle River, NJ, USA, 3rd edition, 2010.