UAV Fleet Selection for Weight, Range, and Release Matching
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Solution Overview
Problem
Existing UAV technologies are not suited to deliver specific items to target locations due to limitations in weight, size, flight distance, and release mechanism compatibility, leading to inaccurate manual selections that result in resource wastage and potential damage to UAVs.
Innovation Solution
A system that determines the attributes of a target location and compares them with UAV attributes to automatically select the most suitable UAV for a mission based on geographical, weight, and release mechanism criteria, using machine learning models to predict successful delivery outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of UAV for delivery is performed, then operational flexibility is maintained, but selection accuracy deteriorates leading to resource wastage and potential UAV damage
Solution Approach 1:
The patent replaces manual selection processes with an automated computer system that uses machine learning models and attribute comparison algorithms to select appropriate UAVs. The system automatically processes delivery parameters, compares them against UAV capabilities, and makes selection decisions without human intervention, thereby eliminating human error and optimizing resource allocation.
Solution Approach 2:
The system enables self-service by allowing the automated selection process to determine the most suitable UAV based on predefined criteria and historical data. The machine learning model continuously learns from past deliveries and autonomously improves selection accuracy over time without requiring manual reconfiguration or expert intervention.
2Adaptability or versatility
If a single UAV type is used for all deliveries, then device complexity is reduced, but adaptability to different delivery requirements deteriorates
Solution Approach 1:
The patent utilizes parameter changes by comparing specific attributes of delivery tasks (weight, dimensions, distance, terrain) with corresponding UAV capabilities. The system dynamically adjusts selection criteria based on the specific parameters of each delivery request, enabling flexible matching without requiring physical modification of UAVs or complex manual assessment procedures.
3Reliability
If inappropriate UAV is selected for a delivery mission, then operational simplicity is maintained, but delivery reliability deteriorates due to mission failure and UAV damage
Solution Approach 1:
The system performs preliminary action by conducting comprehensive attribute comparisons and feasibility assessments before assigning a UAV to a delivery mission. The machine learning model evaluates multiple potential UAVs against delivery requirements in advance, identifying the most suitable candidate and preventing inappropriate assignments before they occur, thereby ensuring high delivery reliability.
Data Source
AI summary
In various aspects, a first set of attributes associated with a target location are determined. The target location is a location that one or more items are to be delivered to by an unmanned aerial vehicle (UAV). The first set of attributes are compared with a second set of attributes. The second set of attributes indicate attributes of each UAV of a plurality of UAVs. Based on the comparing, a UAV, of the plurality of UAVs, is recommended to deliver the one or more items to the target location.