Store Inventory Robot Scanning With Wireless Connectivity Mapping
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Solution Overview
Problem
Current stock keeping methods face challenges in efficiently deploying robotic systems to scan inventory within stores due to limitations in local wireless connectivity, leading to latency and connectivity issues during inventory scanning processes.
Innovation Solution
A method involving a robotic system that autonomously navigates a store, generates spatial and wireless connectivity maps, and calculates optimal routes to align wireless network connectivity requirements, allowing for real-time image capture and upload of inventory data while avoiding low connectivity regions, and recommending additional wireless access points for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the robotic system captures and uploads images in real-time throughout the store, then inventory scanning completeness is improved, but wireless connectivity loss increases due to low connectivity regions
Solution Approach 1:
The system performs a mapping cycle before inventory scanning to pre-characterize wireless connectivity throughout the store. This preliminary action creates a connectivity map that identifies low-connectivity regions in advance, allowing the system to plan scanning routes that avoid these areas and ensure reliable real-time image upload without compromising scanning completeness
Solution Approach 2:
The system dynamically adjusts the scanning route based on the pre-characterized wireless connectivity map. Instead of following a fixed geometric path, the robotic system adapts its navigation to dynamically avoid low-connectivity regions while maintaining comprehensive inventory coverage, balancing scanning completeness with connectivity reliability
2Loss of time
If the robotic system increases image capture frequency to reduce latency, then stock condition derivation speed is improved, but data upload time increases due to wireless connectivity constraints
Solution Approach 1:
The system applies different image capture frequencies to different regions of the store based on their wireless connectivity characteristics. In high-connectivity regions, the system captures images at higher frequency to reduce latency, while in low-connectivity regions, it reduces capture frequency to ensure complete data upload, optimizing the balance between derivation speed and upload time locally for each area
Solution Approach 2:
The system pre-characterizes wireless connectivity at the same spatial resolution as the inventory structures during the mapping cycle. This preliminary characterization allows the system to know in advance which regions can support high-frequency capture and which require reduced frequency, enabling proactive adjustment of capture rates before scanning begins
3Ease of operation
If the robotic system follows a fixed geometric scanning path, then navigation simplicity is improved, but wireless connectivity performance deteriorates due to unavoidable low connectivity regions
Solution Approach 1:
The system transitions from static geometric path planning to dynamic route optimization based on real wireless connectivity data. The scanning path is dynamically adjusted to avoid low-connectivity regions identified during the mapping cycle, maintaining navigation simplicity through automated route calculation while significantly improving connectivity performance by adapting to the actual wireless environment
4Measurement precision
If the robotic system uses high resolution image capture to improve inventory accuracy, then measurement precision is improved, but data upload requirements increase causing latency in low connectivity regions
Solution Approach 1:
The system applies different image capture resolutions to different regions based on wireless connectivity quality. In high-connectivity regions, high-resolution capture is used to maximize inventory detection accuracy. In low-connectivity regions, the system reduces image resolution to minimize data upload requirements and latency, while still maintaining sufficient accuracy for inventory identification
Solution Approach 2:
The system dynamically changes the image capture resolution parameter based on the wireless connectivity characteristics of each region. By adjusting this critical parameter according to local connectivity conditions, the system optimizes the trade-off between measurement precision and upload latency, ensuring high accuracy where possible while maintaining operational efficiency in constrained regions
Data Source
AI summary
One variation of a method for deploying a mobile robotic system to scan inventory structures within a store includes: dispatching the mobile robotic system to navigate along inventory structures within the store during a setup cycle; at the mobile robotic system, while navigating along the inventory structures during the setup cycle, capturing a set of wireless connectivity metrics representing connectivity to a first wireless network; assembling the set of wireless connectivity metrics into a wireless connectivity map of the store; estimating a processing duration from start of the scan cycle to transformation of images of the inventory structures, captured by the mobile robotic system, into a stock condition of the store; and dispatching the mobile robotic system to autonomously capture images of the inventory structures within the store during a scan cycle preceding a scheduled restocking period in the store based on the processing duration.