Aerial Delivery Data Models for Safe Package Surface Selection
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Solution Overview
Problem
Aerial delivery vehicles face challenges in detecting suitable delivery surfaces at geographic locations, as they often encounter obstacles and environmental conditions that hinder safe package placement, and existing methods fail to determine alternative surfaces in real-time.
Innovation Solution
A system generates delivery data models by determining delivery surface data objects, encoding geographic addresses, and associating them with delivery locations, allowing aerial vehicles to safely deliver packages by identifying and ranking potential surfaces based on proximity, environmental conditions, and other criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If aerial delivery vehicles use traditional delivery methods, then delivery operations are simple, but delivery safety and reliability deteriorate due to inability to detect suitable surfaces and obstacles
Solution Approach 1:
The system performs preliminary actions by generating comprehensive delivery data models that include delivery surfaces, restricted surfaces, boundary elements, and approach paths before the actual delivery operation. This advance preparation enables the aerial delivery vehicle to identify suitable delivery locations and potential obstacles in advance, improving delivery safety without requiring complex real-time decision-making during the delivery process.
Solution Approach 2:
The system applies beforehand cushioning by creating a detailed delivery data model that identifies restricted access surfaces and boundary elements that constrain the delivery vehicle's approach. This pre-identified constraint information acts as a protective buffer, preventing the vehicle from attempting deliveries at unsuitable locations or navigating through hazardous areas, thereby enhancing delivery reliability.
2Adaptability or versatility
If aerial delivery vehicles detect delivery surfaces in real-time, then delivery adaptability improves, but detection difficulty and time consumption increase
Solution Approach 1:
The system performs preliminary actions by pre-generating delivery data models that include multiple delivery surfaces and their characteristics before the delivery operation. This advance preparation provides the aerial delivery vehicle with a library of suitable delivery locations and their associated constraints, enabling rapid adaptation to different delivery scenarios without requiring complex real-time surface analysis.
Solution Approach 2:
The system applies copying by creating a digital representation (delivery data model) of the physical delivery environment, including delivery surfaces, restricted surfaces, and boundary elements. This digital copy allows the aerial delivery vehicle to analyze and select appropriate delivery surfaces without directly interrogating the physical environment during the delivery operation, reducing detection complexity while maintaining adaptability.
3Adaptability or versatility
If aerial delivery vehicles follow predetermined delivery paths, then navigation simplicity is maintained, but ability to handle environmental changes deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-identifying multiple delivery surfaces and their characteristics in the delivery data model, including primary delivery surfaces and alternate surfaces. This advance preparation enables the aerial delivery vehicle to adapt to environmental changes by selecting from pre-identified alternative delivery locations without requiring complex real-time path recalculation or navigation system modifications.
Data Source
AI summary
An approach is provided for generating delivery data models for aerial package delivery. The approach involves determining at least one delivery surface data object to represent one or more delivery surfaces of at least one delivery location, wherein the one or more delivery surfaces represents at least one surface upon which to deliver at least one package. The approach further involves causing, at least in part, a creation of at least one complete delivery data model based, at least in part, on the at least one delivery surface data object to represent the at least one delivery location. The approach further involves causing, at least in part, an encoding of at least one geographic address in the at least one complete delivery data model to cause, at least in part, an association of the at least one complete delivery data model with at least one geographic location.