Research on multi-load AGV path planning of weaving workshop based on time priority

Area of Science:

• Industrial Engineering
• Robotics
• Operations Research

Background:

• Intelligent weaving workshops require efficient material handling.
• Automated Guided Vehicles (AGVs) offer a solution for autonomous transport.
• Optimizing AGV path planning is crucial for maximizing operational efficiency.

Purpose of the Study:

• To develop an automated path planning system for multi-load AGVs in intelligent weaving workshops.
• To enhance transportation efficiency and machine utilization through optimized routing.
• To address constraints including environmental, load, and work limitations.

Main Methods:

• Creation of a feasible path topology based on workshop layout and logistics.
• Application of the Warshall-Floyd algorithm for optimal route searching.
• Utilizing a mixed Genetic Algorithm-Particle Swarm Optimization (GA-PSO) for mathematical modeling and problem-solving.

Main Results:

• A novel particle iteration mechanism based on time priority was proposed to enhance directional evolution and convergence speed.
• The GA-PSO algorithm effectively solved the complex path planning problem.
• Simulation and benefit analysis confirmed the model's effectiveness and practicality.

Conclusions:

• The developed multi-load AGV system and GA-PSO path planning method significantly improve material handling efficiency in intelligent weaving.
• The proposed time-priority iteration mechanism accelerates algorithm convergence.
• This research provides a viable solution for autonomous logistics in manufacturing environments.