Shelf Imaging Waypoints From Store Map and Metaspace Alignment
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Solution Overview
Problem
Current stock tracking methods lack efficiency in automatically generating accurate waypoints for imaging shelves within a store, leading to inaccuracies and inefficiencies in product inventory management.
Innovation Solution
A method involving a robotic system that autonomously maps the store floor, aligns architectural metaspace with real map data, and generates normalized waypoints based on shelving structures and imaging capabilities, allowing for precise image capture and inventory analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to generate waypoints for imaging shelves, then flexibility and adaptability are maintained, but productivity and accuracy deteriorate due to time-consuming processes and human error
Solution Approach 1:
The robotic system autonomously generates its own waypoints by integrating its sensor data with architectural metaspace information, eliminating the need for external manual programming or supervision. The system self-calibrates and self-navigates by automatically aligning its perceived environment with the store's architectural blueprint, thereby achieving high productivity without proportionally increasing system complexity.
2Measurement precision
If automated robotic systems are deployed for stock tracking, then productivity and accuracy improve, but device complexity and initial setup requirements worsen
Solution Approach 1:
The architectural metaspace serves as an intermediary layer that bridges the robotic system's sensor perceptions and the physical store environment. By aligning sensor data with pre-stored architectural information, the system achieves high measurement precision without requiring complex real-time processing or extensive system integration, as the metaspace provides a standardized reference framework.
Solution Approach 2:
The architectural metaspace is pre-populated with store layout information, shelving structures, and product locations before the robotic system begins operation. This preliminary preparation enables the robot to quickly generate accurate waypoints and navigate efficiently without requiring complex real-time decision-making, thereby achieving high accuracy while managing system complexity through advance planning.
3Loss of information
If comprehensive waypoint coverage is generated for all shelving structures, then data collection completeness improves, but loss of time and computational resources worsen
Solution Approach 1:
The system generates waypoints with local quality by tailoring the density and distribution of waypoints to specific shelving structures and product categories. High-priority areas with frequently changing inventory receive denser waypoint coverage, while stable areas use sparser sampling. This selective approach ensures data collection completeness for critical regions while minimizing time loss across the entire store.
Solution Approach 2:
The system implements partial action by generating waypoints only for shelving structures that require monitoring based on predefined criteria, rather than uniformly covering every shelf. This selective waypoint generation maintains data collection completeness for relevant areas while significantly reducing the time and computational resources required compared to comprehensive full-store coverage.
Data Source
AI summary
One variation of a method for automatically generating waypoints for imaging shelves within a store includes: dispatching a robotic system to autonomously generating a map of a floor space within the store; accessing an architectural metaspace defining target locations and addresses of the set of shelving structures within the store; distorting the architectural metaspace into alignment with the map to generate a normalized metaspace representing real locations and addresses of the set of shelving structures in the store; defining a set of waypoints distributed longitudinally along and offset laterally from a first shelving structure represented in the normalized metaspace based on a known position of an optical sensor in the robotic system; and dispatching the robotic system to record optical data while occupying the set of waypoints during an imaging routine.