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Indoor Robot Path Planning Using an Improved Whale Optimization Algorithm.

Qing Si1, Changyong Li1

  • 1School of Mechanical Engineering, Xinjiang University, Urumqi 830039, China.

Sensors (Basel, Switzerland)
|April 28, 2023
PubMed
Summary

This study introduces an improved whale optimization algorithm (ILWOA) for robot path planning, enhancing convergence speed and path quality. ILWOA demonstrates superior performance over standard algorithms in complex environments.

Keywords:
Corsi varianceindoor roboticsnonlinear convergence factorpath planningwhale optimization algorithm

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Traditional whale optimization algorithms (WOA) face challenges in indoor robot path planning, including slow convergence, poor pathfinding, and local optima.
  • Existing methods struggle with efficiency and finding globally optimal paths.

Purpose of the Study:

  • To enhance the whale optimization algorithm (WOA) for improved indoor robot path planning.
  • To address limitations such as slow convergence, inefficient pathfinding, and susceptibility to local optima.

Main Methods:

  • Implemented an improved logistic chaotic mapping to enrich the initial whale population and boost global search capabilities.
  • Introduced a nonlinear convergence factor and modified the equilibrium parameter A to balance global and local search, enhancing efficiency.
  • Utilized a fused Corsi variance and weighting strategy to perturb whale locations, thereby improving path quality.

Main Results:

  • The improved logical whale optimization algorithm (ILWOA) exhibited superior convergence and merit-seeking abilities on test functions compared to WOA and other variants.
  • ILWOA outperformed other algorithms across three evaluation criteria in raster map path planning experiments.
  • Experimental results confirmed enhanced path quality, merit-seeking ability, and robustness of ILWOA.

Conclusions:

  • The proposed ILWOA effectively overcomes the limitations of the original WOA in indoor robot path planning.
  • ILWOA demonstrates significant improvements in convergence speed, path quality, and robustness for robot navigation.
  • This enhanced algorithm offers a more efficient and reliable solution for complex path planning tasks.