Liquid Lens Vision for Robot Fleet Object Recognition at the Edge
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Solution Overview
Problem
The proliferation of data from distributed sensors and devices in value chain networks overwhelms the ability to transmit and process data effectively, leading to complexity and missed opportunities for insight and timely decision-making.
Innovation Solution
A method for processing queries in a distributed database using edge devices, involving dynamic ledgers and probability distribution models to generate approximate responses, and optimizing database operations through query prediction and data prioritization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is collected from distributed sensors and devices across value chain networks, then the amount of available data increases dramatically, but the ability to transmit and process data effectively is overwhelmed
Solution Approach 1:
The patent segments the centralized data processing architecture into distributed edge computing nodes deployed across the value chain network. Each edge device independently processes and analyzes data locally, breaking down the monolithic processing complexity into manageable distributed units that can operate autonomously
Solution Approach 2:
The patent introduces a new dimensional approach by implementing hierarchical data processing with multiple levels: edge devices performing initial processing, regional aggregation points consolidating results, and central systems handling strategic analytics. This multi-dimensional architecture distributes the processing load across spatial and organizational dimensions
2Loss of information
If all sensor data is transmitted for processing, then complete information is available for decision-making, but network overhead and transmission time increase significantly
Solution Approach 1:
The patent extracts and processes critical information at the edge devices before transmission. Edge computing nodes perform local analytics to identify and extract only the most relevant insights and anomalies from raw sensor data, transmitting merely these extracted insights to central systems rather than the complete raw data sets
Solution Approach 2:
The patent implements preliminary data processing and filtering at edge devices before data leaves the local network segment. Edge nodes perform initial analytics, validate data quality, and pre-aggregate measurements, so that when data is transmitted to central systems, the preliminary processing work has already been completed locally
3Reliability
If centralized processing is used to maintain system-wide visibility, then coordination across the fleet is improved, but the system becomes less responsive to local conditions and more vulnerable to single points of failure
Solution Approach 1:
The patent implements a dynamic architecture where edge devices operate autonomously with full decision-making capability for local conditions, while simultaneously maintaining communication with central systems for coordination when needed. The system dynamically adjusts between centralized and decentralized modes based on operational context, network conditions, and task requirements
Solution Approach 2:
The patent establishes continuous feedback loops between edge devices and central systems. Edge devices transmit operational status, performance metrics, and local decision outcomes to central systems, which aggregate this feedback to maintain system-wide visibility and coordinate fleet-wide optimization while preserving local autonomy
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
Enables efficient data processing and timely decision-making by reducing network overhead and improving data management in complex network environments.
Implementation Method 1
an optical assembly including a lens containing a liquid, wherein the lens is deformable to generate variable focus
Data Source
AI summary
A dynamic vision system for robot fleet management includes an optical assembly including a lens containing a liquid. The lens is deformable to generate variable focus for the lens. The optical assembly is configured to capture optical data. The dynamic vision system includes a robot fleet management platform having a control system configured to adjust one or more optical parameters. The one or more optical parameters modify the variable focus of the lens while the optical assembly captures current optical data relating to a robotic fleet. The dynamic vision system includes a processing system configured to train a machine learning model to recognize an object relating to the robotic fleet using training data generated from the optical data captured by the optical assembly. The optical data includes the current optical data relating to the robotic fleet.