Autonomous Robotic Planogram Generation
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Solution Overview
Problem
Current methods for generating planograms that assign products to shelving structures within stores are inefficient, often requiring manual intervention and lacking accuracy, especially in environments without pre-existing floor plans or when shelving structures change.
Innovation Solution
A robotic system autonomously navigates and maps a store, capturing optical data to generate a planogram by identifying products and their positions, allowing for automated generation and updating of product assignments on shelves, with human oversight for validation and adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to generate planograms, then flexibility in handling dynamic store environments is maintained, but productivity and accuracy deteriorate due to inefficiency and labor intensity
Solution Approach 1:
The system enables self-service by allowing the robotic system to autonomously navigate, capture images, and generate planograms without continuous human intervention. The robotic system independently performs mapping, image capture, and planogram generation tasks, reducing dependency on manual operations while maintaining adaptability to store environment changes.
Solution Approach 2:
Manual mechanical operations are replaced with an automated robotic system that uses sensors, cameras, and computer vision algorithms. The robotic system substitutes human operators in navigating store aisles, capturing shelf images, and processing planogram data, thereby improving productivity while maintaining operational flexibility through programmable autonomy.
2Measurement precision
If automated robotic systems are deployed, then productivity and accuracy improve, but device complexity increases due to navigation and imaging requirements
Solution Approach 1:
The robotic system is designed as a multi-functional platform that performs navigation, image capture, and planogram generation within a single integrated system. This universal approach consolidates multiple functions into one device, reducing overall system complexity compared to using separate specialized systems for each task while maintaining high measurement precision through coordinated operation of all functions.
Solution Approach 2:
The system employs an intermediary processing layer that bridges the robotic hardware and planogram generation software. This intermediary layer manages the complexity of coordinating navigation, image capture, and data processing by providing standardized interfaces and abstraction layers, thereby simplifying the overall system architecture while enabling precise product position detection.
3Adaptability or versatility
If frequent planogram updates are performed to reflect shelving changes, then adaptability improves, but loss of time increases due to repeated mapping and imaging routines
Solution Approach 1:
The system performs preliminary mapping of the store layout and shelving structures before actual planogram generation. By pre-establishing the spatial framework and shelving configurations, the system reduces the time required for subsequent planogram updates when shelving changes occur, as the robotic system can focus on capturing only the changed areas rather than remapping the entire store.
Solution Approach 2:
The robotic system implements periodic monitoring and imaging routines at scheduled intervals to detect shelving structure changes. This periodic approach allows the system to maintain adaptability by regularly updating planograms without requiring continuous operation, thereby reducing overall time loss while still responding promptly to store environment changes when they occur.
Data Source
AI summary
One variation of a method for automatically generating a planogram for a store includes: dispatching a robotic system to autonomously navigate within the store during a mapping routine; accessing a floor map of the floor space generated by the robotic system from map data collected during the mapping routine; identifying a shelving structure within the map of the floor space; defining a first set of waypoints along an aisle facing the shelving structure; dispatching the robotic system to navigate to and to capture optical data at the set of waypoints during an imaging routine; receiving a set of images generated from optical data recorded by the robotic system during the imaging routine; identifying products and positions of products in the set of images; and generating a planogram of the shelving segment based on products and positions of products identified in the set of images.