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Improved RRT* Algorithm for Disinfecting Robot Path Planning.

Haotian Wang1, Xiaolong Zhou1, Jianyong Li1

  • 1Mechanical Engineering College, Beihua University, Jilin 132021, China.

Sensors (Basel, Switzerland)
|March 13, 2024
PubMed
Summary
This summary is machine-generated.

An improved artificial potential field-guided fuzzy adaptive rapidly exploring random trees (APF-GFARRT*) algorithm enhances path planning for disinfecting robots in complex environments. This method improves search success rates and reduces pathfinding time and cost.

Keywords:
artificial potential field (APF)disinfection robotfuzzy controlpath planningrapidly exploring random trees (RRT*)

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Path Planning Algorithms

Background:

  • Rapidly exploring random trees (RRT) algorithms suffer from weak orientation and low search success rates in dense environments.
  • Disinfecting robots require efficient path planning in complex, confined spaces.

Purpose of the Study:

  • To propose an improved APF-GFARRT* algorithm for enhanced path planning of disinfecting robots.
  • To address the limitations of existing RRT-based algorithms in terms of search efficiency and success rate.

Main Methods:

  • The APF-GFARRT* algorithm integrates artificial potential field (APF) guided sampling with fuzzy adaptive expansion.
  • Fuzzy control is used for adaptive step-size adjustment in dense and restricted environments.
  • The algorithm was validated in MATLAB against RRT, RRT*, and APF-RRT*.

Main Results:

  • The APF-GFARRT* algorithm reduced average initial path search time by 46.52%.
  • It achieved faster convergence, reducing average iteration count and final path cost by 10.01%.
  • The improved algorithm demonstrated superior adaptability, increasing pathfinding success and generating smoother paths.

Conclusions:

  • The APF-GFARRT* algorithm offers a practical and feasible solution for robot path planning in challenging environments.
  • The integration of APF and fuzzy logic significantly enhances exploration speed and path quality.