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Energy-Efficient Collision-Free Machine/AGV Scheduling Using Vehicle Edge Intelligence.

Zhengying Cai1, Jingshu Du1, Tianhao Huang1

  • 1Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipments, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China.

Sensors (Basel, Switzerland)
|January 8, 2025
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Summary
This summary is machine-generated.

This study introduces an edge computing approach for autonomous guided vehicles (AGVs) to achieve collision-free, energy-efficient scheduling. The novel method balances production efficiency with safety and reduced energy use.

Keywords:
artificial plant community algorithmautomatic guided vehiclescollision-free schedulingenergy efficientvehicle edge intelligence

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Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Operations Research

Background:

  • The increasing deployment of autonomous guided vehicles (AGVs) presents significant collision avoidance challenges.
  • Balancing production efficiency, collision prevention, and energy consumption is a complex, multi-objective problem in AGV scheduling.
  • Existing methods often struggle to optimize these conflicting factors simultaneously.

Purpose of the Study:

  • To propose a novel edge computing method leveraging vehicle edge intelligence for energy-efficient, collision-free AGV scheduling.
  • To address the inherent conflicts between production efficiency, collision avoidance, and energy consumption in AGV operations.
  • To develop a solution deployable on embedded platforms for real-time decision-making.

Main Methods:

  • Development of a vehicle edge intelligence architecture with state transition diagrams for collision-free scheduling.
  • Modeling the scheduling problem as a multi-objective function incorporating electric capacity constraints.
  • Exploration of an artificial plant community algorithm utilizing heuristic search and swarm intelligence of AGVs.

Main Results:

  • The proposed edge computing method effectively integrates production efficiency, collision prevention, and energy conservation.
  • The artificial plant community algorithm, applied via AGV edge intelligence, demonstrated successful energy-efficient, collision-free scheduling.
  • Benchmark experiments validated the heuristic method's capability to guide multiple AGVs safely and efficiently.

Conclusions:

  • The novel edge computing approach based on vehicle edge intelligence offers an effective solution for energy-efficient, collision-free AGV scheduling.
  • The method successfully optimizes conflicting objectives, providing a practical framework for AGV deployment.
  • The developed algorithm is suitable for embedded systems, enabling real-time collision avoidance and energy management.