Cover page and Table of Contents: PDF (size: 437KB)
Full Text (PDF, 437KB), PP.20-25
Views: 0 Downloads: 0
Robotic, Path planning, Meta-heuristic, Crow Swarm Optimization (CSO), Optimization.
One of the most common problem in the design of robotic technology is the path planning. The challenge is choosing the robotics’ path from source to destination with minimum cost. Meta-heuristic algorithms are popular tools used in a search process to get optimal solution. In this paper, we used Crow Swarm Optimization (CSO) to overcome the problem of choosing the optimal path without collision. The results of CSO compared with two meta-heuristic algorithms: PSO and ACO in addition to a hybrid method between these algorithms. The comparison process illustrates that the CSO better than PSO and ACO in path planning, but compared to hybrid method CSO was better whenever the smallest population. Consequently, the importance of research lies in finding a new method to use a new meta-humanistic algorithm to solve the problem of robotic path planning.
Mohammed Yousif, Ahmad Salim, Wisam K. Jummar," A Robotic Path Planning by Using Crow Swarm Optimization Algorithm ", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.7, No.1, pp. 20-25, 2021. DOI: 10.5815/ijmsc.2021.01.03
M. Mariappan, T. W. Fang, M. Nadarajan, and N. Parimon, “Face Detection and Auto Positioning for Robotic Vision System.,” International Journal of Image, Graphics & Signal Processing, vol. 7, no. 12, 2015.
A. K. Tiwari and S. V. Nadimpalli, “New Fusion Algorithm provides an alternative approach to Robotic Path planning,” arXiv preprint arXiv:2006.05241, 2020.
A. Agrawal and M. P. Brijpuria, “A Dynamic Object Identification Protocol for Intelligent Robotic Systems,” IJ Image, Graphics and Signal Processing, vol. 8, pp. 35–41, 2015.
M. A. H. Eljinini and A. Tayyar, “Collision-free Random Paths between Two Points,” International Journal of Intelligent Systems and Applications, vol. 12, no. 3, p. 27, 2020.
R. Malik and S. Prasad, “Robot navigation and exploration in an unknown environment,” in Robotic Systems, Springer, 1992, pp. 423–430.
P. Pandey, A. Shukla, and R. Tiwari, “Aerial path planning using meta-heuristics: a survey,” in 2017 second international conference on electrical, computer and communication technologies (ICECCT), 2017, pp. 1–7.
M. Yousif and B. Al-Khateeb, “A Novel Metaheuristic Algorithm for Multiple Traveling Salesman Problem,” Adv. Res. Dyn. Control Syst, vol. 10, no. 13, pp. 2113–2122, 2018.
Y. Gigras, K. Choudhary, K. Gupta, and others, “A hybrid ACO-PSO technique for path planning,” in 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), 2015, pp. 1616–1621.
M. Dorigo, M. Birattari, and T. Stutzle, “Ant colony optimization,” IEEE computational intelligence magazine, vol. 1, no. 4, pp. 28–39, 2006.
J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95-International Conference on Neural Networks, 1995, vol. 4, pp. 1942–1948.
X. Chen, Y. Kong, X. Fang, and Q. Wu, “A fast two-stage ACO algorithm for robotic path planning,” Neural Computing and Applications, vol. 22, no. 2, pp. 313–319, 2013.
A. T. Salawudeen et al., “Recent Metaheuristics Analysis of Path Planning Optimaztion Problems,” in 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS), 2020, pp. 1–7.
S. Raiesdana, “A Hybrid Method for Industrial Robot Navigation,” Journal of Optimization in Industrial Engineering, vol. 14, no. 1, pp. 219–234, 2021.
S. Mirjalili and A. Lewis, “The whale optimization algorithm,” Advances in engineering software, vol. 95, pp. 51–67, 2016.
A. Vadivel, A. K. Majumdar, and S. Sural, “Performance comparison of distance metrics in content-based image retrieval applications,” in International Conference on Information Technology (CIT), Bhubaneswar, India, 2003, pp. 159–164.