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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
Summary
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.
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.
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