Drone Elastic Mapping for Physical Data Cluster Visualization
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Solution Overview
Problem
Visualizing and analyzing multidimensional data objects in 2 or 3 dimensional spaces is challenging due to their complexity, as traditional methods struggle to effectively represent hundreds or thousands of features in a way that is easily interpretable.
Innovation Solution
The use of elastic maps, simulated by computer-controlled drones, which create a network of nodes and edges with configurable elastic couplings to represent data spaces, allowing mobile sensors to collect and adjust their configurations based on environmental data points, thereby visualizing data clusters and patterns in a physical environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional methods are used to represent multidimensional data objects, then the data can be stored and processed, but the visualization and interpretation of hundreds or thousands of features in 2 or 3 dimensional space becomes difficult and ineffective
Solution Approach 1:
The patent applies dimensionality change by transitioning from traditional 2D/3D static visualizations to a dynamic physical space where mobile sensors operate in three-dimensional environment. The elastic map embeds multidimensional data into a curved manifold that is then physically realized through mobile sensors moving in 3D space, allowing complex features to be represented through spatial positions and elastic couplings rather than flat graphical representations.
Solution Approach 2:
The patent creates a physical copy of the abstract elastic map data structure using mobile sensors as tangible representations of nodes and edges. Each mobile sensor corresponds to a node in the elastic map, and the elastic couplings between nodes are physically simulated through controlled interactions between sensors. This physical copying transforms abstract data relationships into observable spatial configurations.
2Loss of information
If elastic maps are used to represent data and clusters in measurement space, then data visualization improves, but the system complexity and computational requirements increase
Solution Approach 1:
The patent replaces the computational mechanical system of elastic map simulations with a physical mechanical system of mobile sensors. Instead of computationally calculating elastic energies and minimizing energy functions, the system uses physical mobile sensors that naturally interact through controlled elastic-like couplings. The physical movement and positioning of sensors automatically embody the elastic map principles, reducing computational complexity while maintaining data representation fidelity.
Solution Approach 2:
The mobile sensors autonomously position themselves in physical space to represent the elastic map structure. Each sensor self-adjusts its position based on the elastic coupling forces derived from the underlying data relationships, without requiring continuous external computational control. The system serves itself by allowing the physical configuration of sensors to naturally emerge from the data structure they represent.
3Productivity
If mobile sensors are deployed to establish initial physical configuration based on elastic map, then real-time data collection capability improves, but the control and coordination complexity increases
Solution Approach 1:
The patent applies dynamics by making the mobile sensor system adaptable and flexible rather than static and rigid. The mobile sensors can dynamically reposition themselves in real-time based on changing data conditions and elastic coupling requirements. This dynamic capability allows the system to maintain optimal data collection configurations while automatically adapting to new information, reducing the need for complex pre-planned coordination protocols.
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
This approach enables effective non-linear dimensionality reduction and data visualization by allowing mobile sensors to cluster around areas of interest, providing real-time data representation and enhancing the visualization of complex data patterns in physical environments.
Implementation Method 1
The elastic connection between the nodes is utilized for creating an energy function that is minimized by simulating a physical system as the elastic connection attempts to maintain a tight relationship to representing the data space
Data Source
AI summary
Methods, systems, and computer program products are provided. Aspects include determining characteristic data associated with a physical environment, based on the characteristic, determining an elastic map for the physical environment, wherein the elastic map comprises a graph comprising a set of nodes and a set of edges, wherein each edge connects two nodes and a set of configurable parameters that define an elastic coupling for each edge, determining an initial physical configuration of the set of nodes in the physical environment based on the elastic map, causing a set of mobile sensors to establish the initial configuration in the physical environment, simulating a mobile sensor elastic coupling between each mobile sensor based on the elastic energy function for each edge, collecting an environmental data point associated with the physical environment, and adjusting a configuration of the set of mobile sensors based on the environmental data point.