Aerial Vehicle Delivery Point Clustering for Precision Routing
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Solution Overview
Problem
Traditional geocoding techniques fail to consider physical attributes, conditions, and preferences when determining location coordinates, leading to inadequate identification of optimal routing or delivery points, especially in scenarios involving temporal, weather, seasonal, or regulatory factors.
Innovation Solution
The system determines routing or delivery points by clustering sensed positions of devices associated with tasks at a location, using geoscans to define hypotheses that account for historical data, environmental conditions, and specific task requirements, allowing for the selection of optimal points based on relevance and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional geocoding techniques are used to determine location coordinates, then the process is simple and fast, but the accuracy and relevance of the identified location points are insufficient
Solution Approach 1:
The patent segments the location determination process into multiple components: geocoding to obtain initial coordinates, clustering to group sensed positions, and hypothesis generation to create multiple location hypotheses. This segmentation allows the system to maintain simplicity where possible while adding complexity only where needed to improve accuracy.
Solution Approach 2:
The patent performs preliminary actions by collecting and clustering sensed positions before final location determination. By pre-processing the position data through clustering and hypothesis generation, the system prepares multiple candidate locations in advance, which can then be evaluated and selected based on relevance to the specific task or delivery point.
2Adaptability or versatility
If traditional geocoding is used, then the implementation is straightforward, but it fails to consider physical attributes, environmental conditions, and task-specific requirements
Solution Approach 1:
The patent implements dynamics by making the location determination process adaptive rather than static. The system dynamically generates multiple location hypotheses based on clustered sensed positions and selects the most relevant hypothesis based on task-specific requirements, environmental conditions, and physical attributes. This allows the system to adapt to different contexts while maintaining a structured approach.
Solution Approach 2:
The patent changes parameters by transitioning from a single fixed coordinate output to multiple probabilistic location hypotheses with associated confidence levels. By representing location uncertainty through probability distributions and allowing multiple hypotheses to coexist, the system can select the most appropriate location based on changing conditions and requirements.
3Measurement precision
If multiple location hypotheses are generated and evaluated, then the accuracy of delivery point identification improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by generating a limited number of location hypotheses rather than exhaustively evaluating all possible locations. The clustering process groups sensed positions to create a manageable set of hypotheses, and the system evaluates only these clustered hypotheses rather than all individual sensed positions, reducing processing requirements while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary clustering of sensed positions before generating hypotheses, which reduces the computational burden of subsequent hypothesis evaluation. By pre-organizing the data into clusters, the system reduces the number of hypotheses that need to be fully evaluated, thereby reducing processing time while maintaining identification accuracy.
4Measurement precision
If geocoding only returns general coordinates, then the data processing is simple, but it cannot identify specific routing points or delivery locations
Solution Approach 1:
The patent segments the location information into multiple levels: general geocoded coordinates, clustered sensed positions, and specific location hypotheses. This segmentation allows the system to process information at different granularities, maintaining simplicity for general location identification while providing detailed specificity when needed for routing and delivery point identification.
Solution Approach 2:
The patent adds another dimension to location representation by introducing probability distributions and confidence levels alongside the coordinate data. This dimensional expansion allows the system to convey both the specific location and the uncertainty associated with it, providing richer information without overwhelming processing requirements through structured probabilistic representation.
Data Source
AI summary
Preferred points or regions in space for performing a task at a location, e.g., the delivery of an item to the location, may be defined based on sensed positions obtained during the prior performance of tasks at the location. The sensed positions may be identified using a GPS sensor or like system. Vectors including coordinates of the sensed position, and uncertainties of such coordinates, may be clustered into groups at the location. Subsequently identified vectors including coordinates and uncertainties may further refine a cluster, or be used to generate a new cluster. A preferred point or region in space may be identified based on such location hypotheses and utilized in the performance of tasks. Some preferred points or regions may be used for routing vehicles to the location, while others may correspond to delivery points for items at the location.