Impact Factor
Call For Paper
Volume 12 Issue 07
July 2026
Author(s)
Abstract
Traditional Automated Guided Vehicle (AGV) Systems Rely On Centralized Controllers And Rigid Programming, Causing Collisions, Torque Overloads, And Operational Downtime When Factory Conditions Change. Nobody Checks If An AGV Mission Is Physically Safe Before Execution—if An Operator Assigns An Impossible Task, The Drivetrain Suffers Damage. We Built BlueFactory Copilot To Solve These Problems. It Is A Lightweight Orchestration Platform For Bonfiglioli-powered AGVs That Runs Four Intelligent Modules In The Background: An LLM-based Natural Language Mission Designer, A Physics-accurate Digital Twin Validator, A Mesh-network Swarm Coordinator, And An IoT-driven Predictive Maintenance Engine. When An Operator Issues A Command, The System Parses Intent Via Meta's Llama-3.3 70B Through Groq Cloud, Simulates The Mission Against Torque/thermal/battery Constraints, Negotiates Paths Peer-to-peer With Other AGVs, And Predicts Mechanical Wear. If A Threat Is Detected—like Torque Overload Or Path Conflict—Copilot Takes Action Autonomously: Flattening Acceleration Curves, Rerouting Via Cooperative A*, Or Scheduling Maintenance. We Tested BlueFactory Copilot Against Dynamic Factory Scenarios That Traditional AGV Controllers Failed To Handle. Our System Reduced Simulated Fleet Collisions By 95%, Cut Unplanned Downtime By 30%, And Maintained Average Inference Latency Under 300ms. The App Uses Zero Local GPU And Only Standard CPU Resources.
Keywords
Paper ID
IJSARTV12I5105327
Publication Date
May 11, 2026
Research Area
Computer Science And Engineering