Optimal Robotic Path Planning Using Intelligents Search Algorithms

Authors

  • Baidaa AlKhlidi Al Furat AlnAwast Technical University
  • Ahmad T. Abdulsadda Al Furat AlnAwast Technical University
  • Ali Al Bakri Al Furat AlnAwast Technical University

DOI:

https://doi.org/10.18196/jrc.26132

Keywords:

Modified Fuzzy, Particle Swarm Optimization, Local Route Management, Intelligent Wireless Robot, Robot Navigation

Abstract

This investigation investigates the application of Adjusted Fuzzy Molecule Swarm Optimization (FPSO) to the versatile robot route issue in arrange to decide the briefest conceivable course with the least time required to travel from a beginning area to a goal area in a deterrent working zone. MPSO is being created in this ponder to progress the capability of customized calculations for a worldwide course. The proposed calculations decipher the environment outline spoken to by the framework show and develop an idea or nearly ideal collision-free way. Reenactment tests appear the viability of the most recent organized calculation for portable robot course arranging. The programs are composed in MATLAB R2019a and run on 2.65 GHz Intel Center i5 and 7 GB Smash computers. Changes proposed in MPSO and cuckoo look calculation fundamentally point to resolve the untimely merging issue related to the beginning PSO. A mistake calculate is demonstrated within the MPSO to guarantee the meeting of the PSO. FPSO points to handle another issue which is the populace may incorporate a few infeasible ways; an updated strategy is tired the FPSO to fathom the issue of the infeasible street. The discoveries illustrate that this calculation has huge potential to fathom the course arranging with satisfactory comes about in terms of decreasing remove and time for execution.

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Published

2021-11-08

How to Cite

[1]
B. AlKhlidi, A. T. Abdulsadda, and A. Al Bakri, “Optimal Robotic Path Planning Using Intelligents Search Algorithms”, J Robot Control (JRC), vol. 2, no. 6, pp. 519–526, Nov. 2021.

Issue

Vol. 2 No. 6 (2021): November

Section

Articles

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