Shelf-Monitoring Robot for Price Sign and Stock Detection
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Solution Overview
Problem
Current methods for monitoring store shelves are inefficient and inaccurate due to manual labor, high costs of electronic labels and camera systems, and limitations in detecting stock levels and layout changes, with existing robotic solutions failing to provide comprehensive and scalable solutions for detecting incorrect or missing price signs, product stock, and spatial errors.
Innovation Solution
An autonomous robotic system equipped with a mobile base, sensors (laser, distance, and image sensors), and deep learning algorithms for navigation and recognition, capable of reading price and product labels, detecting stock levels, and generating dynamic navigation routes to ensure comprehensive shelf monitoring without the need for beacons or environmental modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring of shelves is used, then operational simplicity is maintained, but monitoring accuracy and efficiency deteriorate due to human error and time consumption
Solution Approach 1:
The patent replaces manual mechanical monitoring with an autonomous robotic system equipped with sensors (laser, distance, image sensors) and deep learning algorithms. The robot autonomously navigates store aisles, captures images of shelves, and uses computer vision to detect product stock levels, price signs, and layout changes, eliminating human labor while improving accuracy and reducing time consumption.
Solution Approach 2:
The robotic system performs self-navigation through the store using SLAM (Simultaneous Localization and Mapping) algorithms, automatically captures shelf images, and autonomously processes data to generate monitoring reports. The system serves itself by making independent decisions about navigation paths and monitoring priorities without continuous human intervention, thereby improving efficiency and reducing operational time.
2Measurement precision
If electronic labels and camera systems are deployed, then monitoring capability is improved, but system complexity and installation cost increase
Solution Approach 1:
The autonomous robot performs multiple monitoring functions using a single integrated system: it detects product stock levels, verifies price signs, monitors layout changes, and tracks product placement accuracy all through one robotic platform with multi-functional sensors and deep learning algorithms, avoiding the need for separate electronic label systems and camera installations.
Solution Approach 2:
The patent extracts the monitoring function from fixed infrastructure (electronic labels and mounted cameras) and relocates it to a mobile robotic platform. This allows the system to adapt to different store layouts and shelf configurations without reinstallation, reducing system complexity while maintaining detection capability.
3Area of stationary object
If comprehensive shelf monitoring is implemented, then monitoring coverage is improved, but adaptability to layout changes deteriorates due to fixed infrastructure
Solution Approach 1:
The robotic system dynamically adapts to store layout changes by using SLAM navigation to continuously map and localize itself in the store environment. The robot can autonomously adjust its navigation paths and monitoring coverage areas based on real-time environmental perception, allowing comprehensive monitoring coverage while maintaining high adaptability to weekly layout and planogram changes.
4Ease of operation
If existing robotic solutions are used for customer assistance, then basic navigation capability is provided, but comprehensive shelf monitoring capability is insufficient
Solution Approach 1:
The patent enhances basic robotic navigation with specialized deep learning-based vision systems that substitute simple motion detection with sophisticated image analysis. The system uses convolutional neural networks to accurately detect product stock levels, read price signs, and identify layout changes, transforming a basic navigation robot into a comprehensive monitoring system with high measurement precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves accurate detection of product stock levels, layout, and signage with high precision, ensuring efficient spatial coverage and adaptability to store changes, providing comprehensive and cost-effective monitoring of store shelves.
Implementation Method 1
at least one laser sensor (6) arranged towards the front face of the robot, in this case located on the moving base (3) of the robot, where this laser sensor (6) is arranged to measure distances to the robot's environment
Implementation Method 2
at least one laser sensor (6) arranged towards the front face of the robot
Implementation Method 3
at least one image sensor (4) arranged to capture images of the different areas of the store shelves
Data Source
AI summary
An autonomous robotic system, and method for automatically monitoring the state of shelves in stores, like retail stores or supermarkets are based on a mobile The mobile robot is capable of autonomously navigating the aisles of a store, with the ability to monitor the condition of product shelves. Specifically, the robotic system solves problems associated with the operation of the shelves, mainly with respect to the detection of incorrect or missing price signs, verification of offer signs, detection of product stock, estimation of product layout, and identification of products misplaced or with errors in the spatial extent assigned to the supplier on a shelf.