Autonomous Item Identification Using Location-Filtered Matching
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Solution Overview
Problem
Existing systems lack an efficient method for autonomous identification of items in a dynamic environment, particularly when items with similar physical characteristics are geographically distributed, leading to potential incorrect identification.
Innovation Solution
A system comprising sensors and a positioning system integrated with a mobile computational device and container, which senses physical characteristics of items and determines their location, retrieves stored characteristics from a database to identify items based on proximity, thereby reducing incorrect identification by focusing on items near the container.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If item identification is performed by comparing physical characteristics with all available items in the environment, then identification coverage is improved, but identification accuracy deteriorates due to similar characteristics at different locations
Solution Approach 1:
The system applies local quality by restricting the item identification search to a geographically localized area around the container rather than searching the entire environment. The server determines the container's location using positioning system data, then retrieves characteristics only for items within a predetermined distance threshold. This localized approach maintains identification coverage for relevant items while eliminating false matches from similar items at distant locations, thereby resolving the contradiction between coverage and accuracy.
Solution Approach 2:
The system segments the environment into multiple geographic zones based on container location. Instead of treating the entire environment as a single search space, the server divides it into a relevant zone (items near the container) and irrelevant zones (items far from the container). This segmentation is achieved by comparing the distance between container location and item locations against a threshold, allowing the system to focus computational resources on the relevant segment and improve identification accuracy without sacrificing necessary coverage.
2Quantity of substance
If sensors detect all items in the environment, then detection completeness is improved, but detection precision deteriorates due to similar physical characteristics
Solution Approach 1:
The sensor system applies local quality by activating detection only for items within the geographic vicinity of the container. The server uses positioning data to determine which items fall within a predetermined distance threshold, then instructs sensors to focus detection efforts on those specific items. This localized detection approach ensures completeness for relevant items while avoiding false detections from similar items at distant locations, resolving the contradiction between completeness and precision.
3Reliability
If the system processes characteristics of all available items, then identification thoroughness is improved, but processing efficiency deteriorates
Solution Approach 1:
The system extracts only the necessary subset of item characteristics from the database by filtering items based on their geographic proximity to the container. The server retrieves location data, calculates distances, and extracts characteristics only for items within the predetermined threshold distance. This extraction approach maintains identification thoroughness for relevant items while dramatically reducing the volume of data that requires processing, thereby resolving the contradiction between thoroughness and efficiency.
4Measurement precision
If the container location is determined continuously, then location accuracy is improved, but system complexity increases
Solution Approach 1:
The system implements periodic action by determining container location only at specific trigger moments rather than continuously. The positioning system is activated periodically when items are placed in or removed from the container, or when identification operations are initiated. This periodic location determination maintains sufficient location accuracy for identification purposes while avoiding the unnecessary complexity and resource consumption of continuous tracking, resolving the contradiction between accuracy and system complexity.
Data Source
AI summary
Methods, systems, and machine readable medium are provided for autonomous item identification in an environment including a mobile computational device and a container. One or more sensors are disposed at the container to sense a selected item being placed in the container. In response to sensing the selected item being placed in the container, a location of the container is determined using a positioning system. One or more physical characteristics of the selected item is sensed via the one or more sensors at the container. A set of stored characteristics is retrieved from a database for available items proximate to the location of the container. An identification code for the selected item is identified based on a comparison of the one or more physical characteristics of the selected item with the stored characteristics retrieved from the database of the available items based on the location of the container.