Autonomous Yard Asset Tracking With Freshness-Based Rescanning
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Solution Overview
Problem
Managing trailer assets in logistics yards is challenging due to the difficulty in maintaining accurate data and identifying assets within the yard, especially when assets are not regularly updated or are misidentified, leading to inefficiencies in yard operations.
Innovation Solution
A method and system that utilize an ego vehicle equipped with sensors, such as cameras and localization modules, to compare asset data with stored information, update asset locations, and correct misidentifications by routing the vehicle to rescan assets when data freshness thresholds are exceeded or identification probabilities are low, ensuring accurate asset tracking and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual or periodic asset data collection methods are used in yards, then operational simplicity is maintained, but data freshness and accuracy deteriorate over time
Solution Approach 1:
The system enables self-service through autonomous vehicles that automatically navigate the yard, detect assets using sensors, update database records, and route themselves based on data freshness requirements without human intervention. This continuous autonomous operation maintains both high data accuracy and freshness simultaneously.
Solution Approach 2:
The system implements continuous useful action by deploying autonomous vehicles that operate continuously throughout the yard, constantly detecting assets, updating databases, and maintaining fresh data. The vehicles perform uninterrupted rounds, ensuring data is always current without periodic interruptions.
2Reliability
If autonomous vehicles continuously patrol the yard to maintain fresh asset data, then data freshness is improved, but energy consumption and operational complexity increase
Solution Approach 1:
The system applies local quality by routing autonomous vehicles selectively to specific zones or assets based on data freshness thresholds rather than uniform continuous patrol. Vehicles are dispatched only where data staleness exceeds predetermined thresholds, optimizing energy consumption while maintaining necessary data freshness in critical areas.
Solution Approach 2:
The system changes parameters dynamically by adjusting vehicle routing decisions based on data freshness parameters. When data freshness is sufficient, vehicles remain stationary or reduce activity; when thresholds are exceeded, vehicles are activated and routed to relevant locations, optimizing energy use based on actual data quality needs.
3Measurement precision
If comprehensive sensor scanning is performed at every asset location, then measurement precision is improved, but processing time and computational load increase
Solution Approach 1:
The system applies partial action by performing comprehensive sensor scanning only when necessary - specifically when data freshness thresholds are exceeded or when identification confidence is low. For recently verified assets, minimal scanning is performed, reducing processing time while maintaining precision where it matters most.
Solution Approach 2:
The system uses feedback mechanisms where sensor data quality and identification confidence levels are continuously monitored. When confidence is high, scanning frequency is reduced; when confidence drops or data staleness increases, comprehensive scanning is triggered, optimizing the balance between precision and processing time through adaptive feedback control.
Data Source
AI summary
A method of maintaining trailer asset data in a database of a yard is presented, the method including comparing an update time difference for one or more database entries to a predetermined data refresh threshold, wherein the update time difference for a database entry is a difference between a current time and a time the database entry was last updated; commanding an ego vehicle to move a first asset within the yard from a first location to a second location; then later causing the ego vehicle to move the first asset from the first location to the second location via a route that passes by the first associated location; and updating the first database entry with newer data obtained from at least one sensor located on the ego vehicle when the ego vehicle passes by the first associated location.