Warehouse Robot Navigation Using Rack Counting Without QR Codes
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Solution Overview
Problem
Inventory management in storage sites is labor-intensive and inefficient due to misplaced items occupying space, causing personnel to spend time relocating and tracking missing items, and existing navigation systems require costly and maintenance-intensive code markings like QR codes.
Innovation Solution
A robot equipped with an image sensor and processors that captures and analyzes images of the storage site to identify regularly shaped structures, such as racks, by counting the number of rows and columns to determine its location and navigate autonomously, allowing it to identify target locations without prior mapping or extensive code markings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If code markings like QR codes are used for navigation, then navigation accuracy is improved, but device complexity and maintenance cost increase
Solution Approach 1:
The patent extracts the navigation function from dependent code markings and implements it through autonomous visual recognition of naturally present storage site structures. The robot removes reliance on external code systems by using its own image processing capabilities to identify racks, shelves, and aisles, thereby eliminating the need for QR codes while maintaining navigation accuracy.
Solution Approach 2:
The patent replaces the mechanical code marking system with an optical image recognition system. Instead of using physical QR codes that require scanning, the robot uses an image sensor and computer vision algorithms to visually identify and navigate to target locations based on the geometric features of storage structures, substituting mechanical marking with optical recognition.
2Adaptability or versatility
If manual inventory management is performed, then flexibility is maintained, but productivity and efficiency decrease
Solution Approach 1:
The patent implements self-service through autonomous robotic inventory management. The robot independently navigates the storage site, identifies target locations using image recognition, tracks inventory items, and executes management tasks without human intervention. This automation maintains operational flexibility while dramatically improving productivity by eliminating manual labor constraints.
Solution Approach 2:
The patent replaces manual mechanical inventory management with automated robotic systems equipped with image sensors and processing algorithms. The robot autonomously performs navigation, item identification, and inventory tracking, substituting human physical labor with automated optical and computational systems that enhance both efficiency and adaptability.
3Ease of repair
If image recognition is used for navigation, then maintenance cost is reduced, but measurement precision may worsen
Solution Approach 1:
The patent applies preliminary action by pre-training the image recognition system with extensive datasets of storage site structures during the deployment phase. The robot's visual navigation system is calibrated and optimized before operational use, enabling accurate identification of racks, shelves, and aisles without requiring ongoing maintenance or recalibration, thus maintaining precision while reducing maintenance costs.
Solution Approach 2:
The patent replaces mechanical code marking systems with optical image recognition, eliminating the need for physical code maintenance while using advanced image processing algorithms to ensure accurate location identification. The system substitutes fragile mechanical markers with robust visual recognition that is resistant to wear and environmental factors.
Data Source
AI summary
A robot includes an image sensor that captures the environment of a storage site. The robot visually recognizes regularly shaped structures to navigate through the storage site using various object detection and image segmentation techniques. In response to receiving a target location in the storage site, the robot moves to the target location along a path. The robot receives the images as the robot moves along the path. The robot analyzes the images captured by the image sensor to determine the current location of the robot in the path by tracking a number of regularly shaped structures in the storage site passed by the robot. The regularly shaped structures may be racks, horizontal bars of the racks, and vertical bars of the racks. The robot can identify the target location by counting the number of rows and columns that the robot has passed.