International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
권호 표지
DOI QR코드

DOI QR Code

A Parallel Approach to Navigation in Cities using Reconfigurable Mesh

  • El-Boghdadi, Hatem M. (Faculty of Computer & Information Systems, Islamic University of Madinah) ;
  • Noor, Fazal (Faculty of Computer & Information Systems, Islamic University of Madinah)
  • 투고 : 2021.04.05
  • 발행 : 2021.04.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The subject of navigation has drawn a large interest in the last few years. Navigation problem (or path planning) finds the path between two points, source location and destination location. In smart cities, solving navigation problem is essential to all residents and visitors of such cities to guide them to move easily between locations. Also, the navigation problem is very important in case of moving robots that move around the city or part of it to get some certain tasks done such as delivering packages, delivering food, etc. In either case, solution to the navigation is essential. The core to navigation systems is the navigation algorithms they employ. Navigation algorithms can be classified into navigation algorithms that depend on maps and navigation without the use of maps. The map contains all available routes and its directions. In this proposal, we consider the first class. In this paper, we are interested in getting path planning solutions very fast. In doing so, we employ a parallel platform, Reconfigurable mesh (R-Mesh), to compute the path from source location to destination location. 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 two algorithms for path planning. The first assumes maps with linear streets. The second considers maps with branching streets. In both algorithms, the quality of the path is evaluated in terms of the length of the path and the number of turns in the path.

키워드

과제정보

This work is done under the grant received by Deanship of research at Islamic University of Madinah (IUM). We also give special thanks to the administration of IUM for their support in every aspect.

참고문헌

  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.
  6. H.-C. Lee, Effecient parallel algorithms on recon-gurable mesh architectures, Ph.D. Dissertation, University of Missouri-Rolla, 1996. .
  7. D. Wang, Two algorithms for a reachability problem in one-dimensional space, IEEE Trans. Syst., Man, Cybern. 28, 1998.
  8. F. Dehne, A. Hassenklover and J. Sack, Computing the con-guration space for a robot on a mesh-of-processors, Proc. 1989 ICPP 3, 40-47 1989.
  9. 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
  10. 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.
  11. 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
  12. Otte, M.W. A Survey of Machine Learning Approaches to Robotic Path-Planning. 2009.
  13. 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.
  14. 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
  15. Rodrigo F Berriel, Lucas Tabelini Torres, Vinicius B Cardoso, Ranik Guidolini, Claudine Badue, Alberto F De Souza, and Thiago Oliveira-Santos. Heading direction estimation using deep learning with automatic large-scale data acquisition. 2018.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. Piotr Mirowski, Matthew Koichi Grimes, Mateusz Malinowski, Karl Moritz Hermann, Keith Anderson, Denis Teplyashin, Karen Simonyan, Koray Kavukcuoglu, Andrew Zisserman and Raia Hadsell. Learning to Navigate in Cities Without a Map. In 32nd Conference on Neural Information Processing Systems (NeurIPS 2019), Montreal, Canada, 2019
  23. D. Kim,C. Celio, D. Biancolin, J. Bachrach,and K. Asanovic, "Evaluation of RISC-VRTL with FPGA-Accelerated Simulation." In First Workshop on Computer Architecture Research with RISC-V. 2017.