FST Graduate Research Topic


Research Topic: AGI Research and Development: Smart Assembly Line Optimizer in Industrial IoT
Suggested By: Harris Wang
Last Update: September 28th, 2025

AGI Research and Development: Smart Assembly Line Optimizer in Industrial IoT


 


Constrained Object Hierarchies (COH) provides a neuroscience-inspired theoretical framework for modeling Artificial General Intelligence (AGI). As a flexible and robust foundation, COH supports the representation of general intelligent systems across diverse domains.


Formally, a COH is defined as a 9-tuple:


O = (C, A, M, N, E, I, T, G, D)


Where:



  • C: Components – sub-objects organized hierarchically

  • A: Attributes – state variables describing object properties

  • M: Methods – executable actions associated with components

  • N: Neural Components – adaptive models for learning and inference

  • E: Embedding Neural Component – semantic representation of the COH

  • I: Identity Constraints – rules ensuring object integrity

  • T: Trigger Constraints – event-condition-action rules for dynamic behavior

  • G: Goal Constraints – optimization objectives guiding intelligent behavior

  • D: Constraint Daemons – real-time monitors enforcing system constraints





Based on the COH framework, the modelling process and result of Smart Assembly Line Optimizer in Industrial IoT are as follows:


System Description and Significance: This system optimizes manufacturing processes by coordinating robots, conveyors, and quality control mechanisms in real-time. Its significance lies in enabling flexible, efficient production while maintaining quality standards—key requirements for modern manufacturing competitiveness.


Analysis and Design: The system must coordinate physical components while responding to production variations and quality issues. Identity constraints encode safety requirements and quality standards, while neural components handle visual inspection and predictive maintenance. The hierarchical structure mirrors the physical layout of production systems.


Modelling Solution:



  • C: {RobotArm1, ConveyorBelt, QualityControlCamera, CentralCoordinator}

  • A: robot_joint_angles, belt_speed, defect_count, throughput

  • M: adjust_speed(), recalibrate_robot(), flag_defective_unit()

  • N: VisualQualityInspectionModel, PredictiveMaintenanceModel

  • E: Embedding of overall line efficiency and product quality trend

  • I: Line must stop if human detected in restricted safety zone

  • T: IF defect_rate_increases THEN reduce_belt_speed AND increase_camera_sensitivity

  • G: Maximize_throughput AND Minimize_defect_rate

  • D: AnomalyDetectionDaemon monitoring sensor feeds for unusual vibrations


The COH model demonstrates how intelligent manufacturing systems balance efficiency objectives with safety and quality constraints through hierarchical control and adaptive monitoring.





You may choose this topic and pursue one of the following routes:


📝 Essay



  • Survey existing methodologies for modeling, designing, and implementing similar intelligent systems.

  • Include:

    • Literature synthesis

    • COH framework analysis

    • Critical evaluation of competing approaches




🛠️ Project



  • Design and implement the system using the COH model.

  • Include:

    • System architecture and design

    • Development and testing

    • Demonstration of core functionality




🎓 Thesis



  • Explore COH’s theoretical foundations in depth.

  • Include:

    • Proposed enhancements or extensions

    • Implementation and evaluation

    • Development of an advanced intelligent system grounded in COH principles