FST Graduate Research Topic


Research Topic: Logistics & Warehousing: Autonomous Warehouse Picking System
Suggested By: Harris Wang
Last Update: September 28th, 2025

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 Autonomous Warehouse Picking System in Logistics & Warehousing are as follows:


 


System Description and Significance: Automated warehouse systems coordinate robots to fulfill orders efficiently in large-scale distribution centers. These systems are significant for enabling e-commerce scalability and reducing labor costs while improving order accuracy and speed.


 


Analysis and Design: The system must coordinate multiple robots, manage inventory, and optimize picking routes while ensuring safety and preventing conflicts. Neural components handle object recognition and grasp planning, while identity constraints enforce collision avoidance and operational boundaries.


 


Modelling Solution:


 





  • C: {MobileRobots, StorageShelves, PickingStations, PackingStations, OrderQueue}



  • A: robot_location, order_items, shelf_inventory, station_congestion



  • M: navigate_to_shelf(), pick_item(), transport_to_station()



  • N: ComputerVisionForItemGrasping, MultiAgentPathPlanningAlgorithm



  • E: Embedding of overall warehouse throughput and order fulfillment status



  • I: Two robots cannot occupy same physical space (collision_avoidance)



  • T: IF order_is_priority THEN reassign_robots_to_fulfill_it_first



  • G: Maximize_orders_fulfilled_per_hour AND Minimize_robot_congestion



  • D: BottleneckDetectionDaemon identifying slow-moving areas for dynamic reassignment




 


The COH model demonstrates multi-agent coordination in physical environments, using hierarchical control to scale to large numbers of robots while maintaining safety and efficiency through constraint governance.


 






 


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