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Workshop AGV path planning based on improved A* algorithm.

Na Liu1, Chiyue Ma1, Zihang Hu1

  • 1College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.

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|March 8, 2024
PubMed
Summary
This summary is machine-generated.

This study enhances the A* algorithm for automated guided vehicles (AGVs) in digital workshops, significantly reducing path turns and travel time for improved logistics efficiency.

Keywords:
AGVbezier curveimproved A* algorithmpath optimizationpath planning

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Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Operations Research

Background:

  • Automated guided vehicles (AGVs) are crucial for logistics in digital production workshops.
  • Traditional A* algorithm faces challenges with excessive path turns and long transportation times.
  • Optimizing AGV path planning is essential for enhancing workshop efficiency.

Purpose of the Study:

  • To propose an improved A* algorithm for AGV path planning in digital workshops.
  • To address issues of excessive turns and prolonged transit durations.
  • To enhance the overall quality of logistics paths for AGVs.

Main Methods:

  • Internal improvements to the A* algorithm: enhanced node search, diagonal distance heuristic, and turning weights.
  • External optimization: improved forward search and Bessel curve method applied to the A* output path.
  • Comparative analysis against six conventional path planning methods.

Main Results:

  • The internally improved A* algorithm outperforms six conventional methods.
  • The fully improved A* algorithm reduces turning angle by ~69% and path length by ~10%.
  • AGV running time is reduced by 12s compared to the traditional A* algorithm, boosting logistics efficiency.

Conclusions:

  • The proposed enhanced A* algorithm significantly improves AGV path quality in digital workshops.
  • The method effectively reduces path turns and length, leading to shorter travel times.
  • This optimization enhances overall logistics efficiency and reduces operational costs in automated production environments.