Open Access
Issue
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
Article Number 04020
Number of page(s) 5
Section Circuit Simulation, Electric Modules and Displacement Sensor
DOI https://doi.org/10.1051/matecconf/201823204020
Published online 19 November 2018
  1. Rathbun D, Kragelund S, Pongpunwattana A, et al. An evolution-based path planning algorithm for autonomous motion of a UAV through uncertain environments[J]. Algebra Universalis, 2002, 2:551--607. [Google Scholar]
  2. J. C. Latombe, Robot Motion Planning, Kluwer Academic Publishers, 1991. [Google Scholar]
  3. H. Shwail S, Karim A, Turner S. Probabilistic Multi Robot Path Planning in Dynamic Environments: A Comparison between A* and DFS[J]. International Journal of Computer Applications, 2013, 82(7):29-34. [CrossRef] [Google Scholar]
  4. Stentz A. Optimal and efficient path planning for partially-known environments[C]// IEEE International Conference on Robotics and Automation, 1994. Proceedings. IEEE, 2002:3310-3317 vol.4 [Google Scholar]
  5. Skiena S. Dijkstra’s algorithm[J]. Implementing Discrete Mathematics: Combinatorics and Graph Theory with Mathematica, Reading, MA: Addison-Wesley, 1990: 225-227. [Google Scholar]
  6. Bundy A, Wallen L. Breadth-first search[M]//Catalogue of Artificial Intelligence Tools. Springer, Berlin, Heidelberg, 1984: 13-13. [Google Scholar]
  7. Amato N M, Wu Y. A randomized roadmap method for path and manipulation planning[C]//Robotics and Automation, 1996. Proceedings., 1996 IEEE International Conference on. IEEE, 1996, 1: 113–120. [Google Scholar]
  8. Kuffner J J, LaValle S M. RRT-connect: An efficient approach to single-query path planning[C]//Robotics and Automation, 2000. Proceedings. ICRA’00. IEEE International Conference on. IEEE, 2000, 2: 995-1001. [Google Scholar]
  9. Stentz A. Optimal and efficient path planning for partially-known environments[C]//Robotics and Automation, 1994. Proceedings., 1994 IEEE International Conference on. IEEE, 1994: 3310–3317. [Google Scholar]
  10. Ahn C W, Ramakrishna R S. A genetic algorithm for shortest path routing problem and the sizing of populations[J]. IEEE transactions on evolutionary computation, 2002, 6(6): 566-579. [CrossRef] [Google Scholar]
  11. Jensen R M, Bryant R E, Veloso M M. SetA*:an efficient BDD-based heuristic search algorithm[C]// Eighteenth National Conference on Artificial Intelligence and Fourteenth Conference on Innovative Applications of Artificial Intelligence, July 28 -August 1, 2002, Edmonton, Alberta, Canada. DBLP, 2002:668-673. [Google Scholar]
  12. Zeng W, Church R L. Finding shortest paths on real road networks: the case for A*[J]. International Journal of Geographical Information Systems, 2009, 23(4):531-543. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.