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Reseach Article

Pathway Scheming via Environment Stimulated Algorithms

by Anupama Sharma, Sampada Satav
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 51 - Number 12
Year of Publication: 2012
Authors: Anupama Sharma, Sampada Satav
10.5120/8097-1683

Anupama Sharma, Sampada Satav . Pathway Scheming via Environment Stimulated Algorithms. International Journal of Computer Applications. 51, 12 ( August 2012), 32-34. DOI=10.5120/8097-1683

@article{ 10.5120/8097-1683,
author = { Anupama Sharma, Sampada Satav },
title = { Pathway Scheming via Environment Stimulated Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 51 },
number = { 12 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 32-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume51/number12/8097-1683/ },
doi = { 10.5120/8097-1683 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:50:46.139042+05:30
%A Anupama Sharma
%A Sampada Satav
%T Pathway Scheming via Environment Stimulated Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 51
%N 12
%P 32-34
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Pathway scheming algorithm is based on the calculation of the shortest distance between the foundation point and the aim point. And also we consider obstacle, in which the pathway should not crash with the obstacles and also find the shortest coldness so that the smallest amount strength is consumed. in the direction of settle on the direct coldness and keep away from the collision we have taken into consideration BFO algorithms i. e. environment stimulated algorithms, BFOA is inspired by the communal foraging performance of Escherichia coli. BFOA has already drawn the concentration of researchers because of its effectiveness in solving real-world optimization problems arising in several application domains. The intention meaning used to work out the minimum coldness is the Euclidean coldness between the point To avoid the obstacles various constraint have been applied. At the end, the pathway is generated which is collision free and the pathway is straight between the foundation point and the aim point.

References
  1. Chakraborty, J. , Konar, A. , Chakraborty, U. , Jain, L. : Distributed cooperative multi-robot path planning using differential evolution. In: Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on. . IEEE.
  2. S. Doctor, G. K. Venayagamoorthy and V. Gudise, "Optimal PSO for Collective Robotic Search Applications," IEEE Congress on Evolutionary Computations, June 19-23, 2004, Portland OR, USA,
  3. Doctor, S. , Venayagamoorthy, G. : Unmanned vehicle navigation using swarm intelligence. In: Proc. of Int. Conf. on Intelligent Sensing and Information Processing. (2004).
  4. Doctor, S. , Venayagamoorthy, G. : Unmanned vehicle navigation using swarm intelligence. In: Proc. of Int. Conf. on Intelligent Sensing and Information Processing. (2004).
  5. Chen, X. , Li, Y. : Smooth path planning of a mobile robot using stochastic particle swarm optimization. In: Mechatronics and Automation, Proceedings of the 2006 IEEE International Conference on, IEEE (2006).
  6. Hao, Y. , Zu, W. , Zhao, Y. : Real-Time Obstacle Avoidance Method based on Polar Coordination Particle Swarm Optimization in Dynamic Environment. In: Industrial Electronics and Applications, 2007. ICIEA 2007. 2nd IEEE Conference on. IEEE (2007).
  7. Wang, L. , Liu, Y. , Deng, H. , Xu, Y. : Obstacle-avoidance path planning for soccer robots using particle swarm optimization. In: Robotics and Biomimetics, 2006. ROBIO'06. IEEE International Conference on. IEEE (2006).
  8. Li, L. , Ye, T. , Tan, M. , Chen, X. : Present state and future development of mobile robot technology research. Robot 24(5), (2002)
  9. Koren, Y. , Borenstein, J. : Potential field methods and their inherent limitations for mobile robot navigation. In: Robotics and Automation, 1991. Proceedings. 1991 IEEE.
  10. Berg, H. C. (2003)E. coli in motion. Springer-Verlag, NY.
  11. Swagatam Das, Arijit Biswas, Sambarta Dasgupta, and Ajith Abraham, "Bacterial Foraging Optimization Algorithm: Theoretical Foundations,Analysis,andApplications", foundation of computer Intel. Volume-3 ,SCI 203,2009.
  12. J. Barraquand and J. C. Latombe, "Robot motion planning: A distributed representation approach," Int. J. Robot. Res. ,vol. 10, no. 6, 1991
  13. M. Gerke and H. Hoyer, "Planning of optimal paths for autonomous agents moving in inhomogeneous environments," in Proceedings of the 8th International Conference on Advanced Robotics , July 1997.
  14. Z. Bien and J. Lee, "A Minimum-Time Trajectory Planning Method for Two Robots," IEEE Trans. on Robotics and Automation , vol. 8, , June 1992.
  15. J. Kennedy and R. Eberhart, "Particle swarm optimization," Proceedings, IEEE International Conference
Index Terms

Computer Science
Information Sciences

Keywords

NIA course plotting chemotaxix reproduction elimination dispersal E. coli flagella tumble swims