International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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A Parallel Algorithm for Finding Routes in Cities with Diagonal Streets

  • Hatem M. El-Boghdadi (Faculty of Computer & Information Systems, Islamic University of Madinah)
  • Received : 2024.01.05
  • Published : 2024.01.30
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Abstract

The subject of navigation has drawn a large interest in the last few years. The navigation within a city is to find the path between two points, source location and destination location. In many cities, solving the routing problem is very essential as to find the route between different locations (starting location (source) and an ending location (destination)) in a fast and efficient way. This paper considers streets with diagonal streets. Such streets pose a problem in determining the directions of the route to be followed. The paper presents a solution for the path planning using the reconfigurable mesh (R-Mesh). R-Mesh is a parallel platform that has very fast solutions to many problems and can be deployed in moving vehicles and moving robots. This paper presents a solution that is very fast in computing the routes.

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References

  1. Jaja J, An introduction to parallel algorithms. Addison Wesley, Redwood City, CA, USA, 1992. 
  2. Bondalapati K, Prasanna VK. Reconfigurable computing: architectures, models and algorithms. Curr Sci 78:828-837, 2009. 
  3. R. Vaidyanathan and J. L. Trahan, Dynamic Reconfiguration: Architectures and Algorithms (Kluwer Academic/Plenum Publishers), 2004. 
  4. D. Wang, A linear-time algorithm for computing collision-free path on reconfigurable mesh, J. Parallel Comput. 34, 487-496, 2008.  https://doi.org/10.1016/j.parco.2008.03.002
  5. Hatem M. El-Boghdadi. Constant Time Algorithm for Computing a Collision-Free Path on R-Mesh with Path Quality Analysis. Journal of Circuits, Systems, and Computers 24(8): 1550112:1-1550112:20. 2015.  https://doi.org/10.1142/S0218126615501121
  6. Hatem M. El-Boghdadi and Fazal Noor. A Parallel Approach to Navigation in Cities using Reconfigurable Mesh. IJCSNS International Journal of Computer Science and Network Security, VOL.21 No.4, April 2021 
  7. H.-C. Lee, Effecient parallel algorithms on recon-gurable mesh architectures, Ph.D. Dissertation, University of Missouri-Rolla, 1996. .
  8. D. Wang, Two algorithms for a reachability problem in one-dimensional space, IEEE Trans. Syst., Man, Cybern. 28, 1998. 
  9. F. Dehne, A. Hassenklover and J. Sack, Computing the con-guration space for a robot on a mesh-ofprocessors, Proc. 1989 ICPP 3, 40-47 1989. 
  10. P. Tzionas, A. Thanailakis and P. Tsalides, Collision-free path planning for a diamondshaped robot using two dimensional cellular automata, IEEE Trans. Robot. Automat. 13, 237-250, 1997.  https://doi.org/10.1109/70.563646
  11. J. Jenq and W. Li, Computing the configuration space for a convex Robot on hypercube multiprocessors, Proc. 7th IEEE Symp. Parallel and Distributed Processing, pp. 160-167, 1995. ss 
  12. Michael Hoy, Alexey S. Matveev and Andrey V. Savkin. Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey. Robotica, volume 33, pp. 463-497, 2015.  https://doi.org/10.1017/S0263574714000289
  13. Otte, M.W. A Survey of Machine Learning Approaches to Robotic Path-Planning. 2009. 
  14. Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, KateSaenko, andTrevorDarrell. Long-term recurrent convolutional networks for visual recognition and description. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2625-2634, 2015. 
  15. Mateusz Malinowski, Marcus Rohrbach, and Mario Fritz. Ask your neurons: A deep learning approach to visual question answering. International Journal of Computer Vision,125(1-3):110- 135, 2017.  https://doi.org/10.1007/s11263-017-1038-2
  16. Rodrigo F Berriel, Lucas Tabelini Torres, Vinicius B Cardoso, Ranik Guidolini, Claudine Badue, Alberto F De Souza, and Thiago OliveiraSantos. Heading direction estimation using deep learning with automatic large-scale data acquisition. 2018. 
  17. Aditya Khosla, Byoungkwon An An, Joseph J Lim, and Antonio Torralba. Looking beyond the visible scene. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3710-3717, 2014. 
  18. Guillaume Lample and Devendra Singh Chaplot. Playing FPS games with deep reinforcement learning. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017. 
  19. Piotr Mirowski, Razvan Pascanu, Fabio Viola, Hubert Soyer, Andrew Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent Sifre, Koray Kavukcuoglu, Dharshan Kumaran, and Raia Hadsell. Learning to navigate in complex environments. arXiv preprint arXiv:1611.03673, 2016. 
  20. Yi Wu, Yuxin Wu, Georgia Gkioxari, and Yuandong Tian. Building generalizable agents with a realistic and rich 3d environment. arXiv preprint arXiv:1801.02209, 2019. 
  21. Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J. Lim, Abhinav Gupta, Li Fei-Fei, and Ali Farhadi. Target-driven visual navigation in indoor scenes using deep reinforcement learning. In 2017 IEEE International Conference on Robotics and Automation, ICRA, pages 3357- 3364, 2017. 
  22. Michael J Milford, Gordon F Wyeth, and David Prasser. Ratslam: a hippocampal model for simultaneous localization and mapping. In Robotics and Automation, 2004. Proceedings. ICRA'04. 2004 IEEE International Conference on, volume 1, pages 403-408. IEEE, 2004.