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Fully Autonomous On-site Ops: AI Judgment × Robot Auto-Response.

Immediate detection of on-site anomalies and situation assessment
Action proposal by LLM and robot automatic response
Continuous knowledge accumulation through Human in the Loop

For Various Industries Industry-Specific Use Cases

01. Manufacturing (Line Anomaly Detection / Equipment Maintenance)

Use Cases: Real-time detection of manufacturing line operational anomalies or foreign object contamination from camera footage.; Based on vibration and temperature changes in specific equipment, the LLM assesses the failure risk and proposes predictive maintenance actions.; Detects misalignment or stoppage of a robot arm and automatically makes fine adjustments to the arm position or issues recovery instructions via ROS.

Effects: Minimizes production line downtime and contributes to improved productivity.; Reduces high repair costs caused by sudden failures through predictive maintenance.; Accelerates initial response when quality issues occur, reducing the risk of defective products being shipped.

02.Construction and Infrastructure (Safety Management / Remote Inspection)

Use Cases: Detects hazardous behavior of workers on construction sites (e.g., not wearing safety harnesses) via camera footage and sends immediate alerts to managers and on-site robots.; Detects changes such as cracks or rust from drone footage of infrastructure (bridges, wind turbines).; Proposes and executes detailed photography or simple repair actions remotely to inspection robots in response to detected anomalies.

Effects: Significantly reduces the risk of industrial accidents and enhances site safety.; Improves the efficiency of wide-ranging infrastructure inspection work and standardizes inspection quality.; Reduces access to hazardous areas, cutting inspection costs and human risks.

03.Warehousing and Logistics (Inventory Anomaly Detection / Sorting Optimization)

Use Cases: Detects package collapse or anomalies in inventory quantity/arrangement in specific warehouse areas using surveillance cameras.; The LLM analyzes sorting robot operational errors or a sudden increase in cargo volume to be processed, and proposes corresponding actions (speed adjustment, route change).; Activates AGVs (Automated Guided Vehicles) to automatically re-arrange or tidy up cargo in response to detected anomalies.

Effects: Improves inventory management accuracy and operational efficiency within the warehouse.; Eliminates sorting bottlenecks during peak seasons, improving logistics throughput.; Reduces the human resources and time required to handle troubles such as package collapse.

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