Industrial Identification, Sensing and State Estimation

Physical AI and AIoT Research Team of
Aperture Venture Studio, GAO RFID Inc. and GAO Tek Inc.
This program supports the Identification Engine and Sensing Engine. Its purpose is to establish a reliable understanding of physical assets, equipment, people and operating conditions.

Multimodal Industrial Identification, Localization and State Estimation

Develop methods that combine RFID, UWB, BLE, cameras and industrial sensors to determine what an object is, where it is, what condition it is in and what it is doing. The research should address cases where individual technologies provide incomplete or contradictory information.

For example, RFID may identify a component, UWB may locate its carrier, and a camera may determine whether it has entered a machine. The system must associate these observations with the correct physical object.

Research questions

How should observations with different accuracies and timestamps be combined? How can the system maintain identity during missed readings, occlusion or movement between zones?

Initial demonstration

Track tagged materials through receiving, storage and a simulated production station.

Evaluation

Identification accuracy, location error, event-detection accuracy, uncertainty calibration and robustness to missing sensors.

AIoT-Based Inventory, Asset Movement and Process Understanding

Move beyond recording asset locations to understanding inventory changes and operational events. The system should infer arrivals, departures, transfers, consumption, misplaced items and work-in-progress transitions.

This combines your original inventory-management and asset-tracking topics. Automated monitoring is the initial capability; later physical-action extensions can trigger replenishment or dispatch material-handling equipment.

Research questions

Can inventory movements be reconstructed despite missed or duplicate reads? Can the system distinguish a legitimate transfer from an unexplained disappearance?

Initial demonstration

A stockroom or production-material area where the system maintains inventory and explains discrepancies.

Evaluation

Inventory accuracy, event precision and recall, discrepancy-resolution time and manual reconciliation effort.

Industrial Sensor Reliability and Data-Quality Intelligence

Develop methods to determine whether incoming measurements are trustworthy before they influence an AI decision. Address sensor drift, calibration errors, communication gaps, incorrect units, timestamp misalignment and device substitution.

This deserves its own topic because a system must distinguish a failing machine from a failing sensor. It also provides a reusable capability across Aperture’s portfolio.

Research questions

Can redundant measurements and physical relationships reveal faulty sensors? How should unreliable data affect model confidence and permission to act?

Initial demonstration

Introduce sensor faults and missing readings into an instrumented test rig.

Evaluation

Sensor-fault detection, false alarms, fault-isolation accuracy and downstream decision quality.