Page 103 - 2023-Vol19-Issue2
P. 103
99 | Al-Ansarry, Al-Darraji & Honi
[3] D. B. Johnson, “A note on dijkstra’s shortest path al- [14] R. Szczepanski, T. Tarczewski, and K. Erwinski, “En-
gorithm,” Journal of the ACM (JACM), vol. 20, no. 3, ergy efficient local path planning algorithm based on
pp. 385–388, 1973. predictive artificial potential field,” IEEE Access, vol. 10,
pp. 39729–39742, 2022.
[4] S. LaValle, “Rapidly-exploring random trees: A new
tool for path planning,” Research Report 9811, 1998. [15] Y. Singh, S. Sharma, R. Sutton, and D. Hatton, “To-
wards use of dijkstra algorithm for optimal navigation
[5] S. Al-Ansarry and S. Al-Darraji, “Hybrid rrt-a*: An of an unmanned surface vehicle in a real-time marine
improved path planning method for an autonomous mo- environment with results from artificial potential field,”
bile robots.,” Iraqi Journal for Electrical & Electronic 2018.
Engineering, vol. 17, no. 1, 2021.
[16] B. Kasmi and A. Hassam, “Comparative study between
[6] J. Barraquand, B. Langlois, and J.-C. Latombe, “Numer- fuzzy logic and interval type-2 fuzzy logic controllers for
ical potential field techniques for robot path planning,” the trajectory planning of a mobile robot,” Engineering,
IEEE transactions on systems, man, and cybernetics, Technology & Applied Science Research, vol. 11, no. 2,
vol. 22, no. 2, pp. 224–241, 1992. pp. 7011–7017, 2021.
[7] L. Li-sang, L. Jia-feng, Y. Jin-xin, H. Dong-wei, Z. Ji- [17] X. Chen, X. Chen, and G. Xu, “The path planning algo-
shi, J. Huang, and P. Shi, “Path planning for smart car rithm studying about uav attacks multiple moving tar-
based on dijkstra algorithm and dynamic window ap- gets based on voronoi diagram,” International Journal
proach,” Wireless Communications & Mobile Comput- of Control and Automation, vol. 9, no. 1, pp. 281–292,
ing (Online), vol. 2021, 2021. 2016.
[8] S. J. Fusic, P. Ramkumar, and K. Hariharan, “Path plan- [18] W. Yang, P. Wu, X. Zhou, H. Lv, X. Liu, G. Zhang,
ning of robot using modified dijkstra algorithm,” in 2018 Z. Hou, and W. Wang, “Improved artificial potential
National Power Engineering Conference (NPEC), pp. 1– field and dynamic window method for amphibious robot
5, IEEE, 2018. fish path planning,” Applied Sciences, vol. 11, no. 5,
p. 2114, 2021.
[9] D. S. Nair and P. Supriya, “Comparison of temporal
difference learning algorithm and dijkstra’s algorithm [19] G. Klanc?ar, A. Zdes?ar, and M. Krishnan, “Robot navi-
for robotic path planning,” in 2018 Second International gation based on potential field and gradient obtained by
Conference on Intelligent Computing and Control Sys- bilinear interpolation and a grid-based search,” Sensors,
tems (ICICCS), pp. 1619–1624, IEEE, 2018. vol. 22, no. 9, p. 3295, 2022.
[10] S. M. Ahmadi, H. Kebriaei, and H. Moradi, “Con- [20] J. Luo, Z.-X. Wang, and K.-L. Pan, “Reliable path
strained coverage path planning: evolutionary and clas- planning algorithm based on improved artificial poten-
sical approaches,” Robotica, vol. 36, no. 6, pp. 904–924, tial field method,” IEEE Access, vol. 10, pp. 108276–
2018. 108284, 2022.
[11] X. Li, “Path planning of intelligent mobile robot based [21] H. Qin, S. Shao, T. Wang, X. Yu, Y. Jiang, and Z. Cao,
on dijkstra algorithm,” in Journal of Physics: Confer- “Review of autonomous path planning algorithms for
ence Series, vol. 2083, p. 042034, IOP Publishing, 2021. mobile robots,” Drones, vol. 7, no. 3, p. 211, 2023.
[12] H. I. Kang, B. Lee, and K. Kim, “Path planning al-
gorithm using the particle swarm optimization and the
improved dijkstra algorithm,” in 2008 IEEE Pacific-Asia
Workshop on Computational Intelligence and Industrial
Application, vol. 2, pp. 1002–1004, IEEE, 2008.
[13] Z. Cheng, H. Zhang, and Q. Zhao, “The method based
on dijkstra of multi-directional ship’s path planning,”
in 2020 Chinese Control And Decision Conference
(CCDC), pp. 5142–5146, IEEE, 2020.
