Aerial Delivery Point Selection Using Parcel Map Preprocessing
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Solution Overview
Problem
Unmanned Aerial Systems (UAS) face challenges in efficiently determining clear and accessible landing and takeoff spaces, particularly in unfamiliar areas, due to computationally expensive and data-intensive methods using 2D imagery and 3D models, which consume valuable flight time.
Innovation Solution
A method that processes publicly available 2D parcel maps to determine deliverable area shapes within parcels, excluding structure footprints, and ranks areas based on aerial item delivery availability, using criteria such as navigation feasibility and user preferences, without requiring extensive image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 2D imagery and 3D models are used to determine clear and accessible landing spaces, then measurement precision is improved, but computational complexity and data requirements increase significantly
Solution Approach 1:
The patent segments the analysis process into two distinct phases: (1) pre-computation of parcel maps and structure footprints using 2D imagery and 3D models, which is performed offline; and (2) real-time determination of clear and accessible spaces during flight, which only requires simple geometric operations. This segmentation allows high-precision measurements to be achieved while keeping onboard computational requirements minimal.
Solution Approach 2:
The patent performs preliminary actions by pre-processing the environmental data before flight. Parcel maps, structure footprints, and potential landing zone geometries are all computed and stored in advance. During the actual flight mission, the UAS only needs to query these pre-computed datasets and perform simple spatial operations, rather than processing raw 2D imagery and 3D models in real-time.
2Measurement precision
If 2D imagery and 3D models are processed to identify landing areas, then measurement precision is improved, but flight time is consumed due to computational processing
Solution Approach 1:
The patent performs all computationally intensive processing of 2D imagery and 3D models before the flight mission. Parcel maps, structure footprints, and potential landing zone candidates are pre-computed and stored onboard. During flight, the system only performs simple geometric queries and spatial operations on these pre-processed datasets, reducing real-time computational requirements to minimal levels and preserving flight time.
Solution Approach 2:
The patent separates the computational workload into offline pre-processing (performed before flight) and online real-time processing (performed during flight). The complex image processing and 3D model analysis are segmented into the offline phase, while the online phase only requires simple geometric operations, thus eliminating the trade-off between measurement precision and flight time consumption.
3Reliability
If comprehensive area mapping is performed to ensure clear and accessible spaces, then reliability of delivery location is improved, but productivity decreases due to computational overhead
Solution Approach 1:
The patent performs comprehensive area mapping and reliability assessment in advance. Parcel maps, structure footprints, and potential landing zones are all identified and evaluated before flight. During the actual delivery mission, the system only needs to query these pre-evaluat ed datasets and perform simple spatial operations, maintaining high reliability while achieving fast execution and high productivity.
Solution Approach 2:
The patent segments the reliability assessment process into offline comprehensive analysis (performed before flight) and online verification (performed during flight). The complex geometric operations, visibility analysis, and clearance verification are all performed offline, while online operations only require simple queries and basic validation, thus maintaining high reliability without compromising delivery efficiency.
Data Source
AI summary
Disclosed are systems and methods to determine preferred delivery points within a parcel that are available to receive an aerial delivery of an item. For each delivery point a plurality of criteria scores may be determined for various criteria based on a processing of parcel data, image data, and/or sensor data corresponding to the parcel. The criteria may be aerial navigation related criteria or user preference criteria. The criteria scores may then be used to determine a suitability score for each delivery point. In some implementations, a user may specify a preferred delivery point and/or indicate one user criteria as more important than another criteria.