Delivery Vehicle Drop-Off Mapping for Precise Package Placement
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Solution Overview
Problem
Current package delivery systems rely on human judgment and intuition for locating delivery destinations and selecting drop-off spots, which can lead to inefficiencies and potential issues like theft or obstruction, as they lack precise control over delivery locations and times.
Innovation Solution
A system using client computing devices to create virtual models of delivery destinations, allowing recipients to designate precise target drop-off spots, and unmanned delivery vehicles equipped with sensors to navigate and align with these models for accurate package placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human judgment and intuition are used for locating delivery destinations and selecting drop-off spots, then the system is simple to operate, but the precision of delivery location and control over delivery times are insufficient
Solution Approach 1:
The patent creates virtual models (copies) of the delivery destination environment that can be viewed and interacted with on client computing devices. These virtual models include spatial representations of the delivery location, allowing recipients to designate precise drop-off spots within the virtual model. The system then uses these virtual model coordinates to guide the unmanned vehicle to the exact physical location, thereby achieving high delivery location precision without requiring complex manual navigation procedures.
Solution Approach 2:
The patent replaces the mechanical system of human judgment and manual location selection with an automated system using virtual models, sensors, and coordinate mapping. Instead of relying on human intuition to locate and mark drop-off spots, the system uses computational virtual models that can be precisely navigated to, combined with sensor data from the unmanned vehicle to automatically determine and execute the delivery location, achieving high precision through computational rather than mechanical means.
2Productivity
If human delivery personnel are used, then flexibility in handling various delivery scenarios is improved, but productivity and consistency of delivery operations decrease
Solution Approach 1:
The patent enables the delivery system to serve itself by allowing recipients to autonomously designate their preferred drop-off locations through interaction with virtual models on their own computing devices. The system automatically processes these designations, stores them as target coordinates, and uses them to guide unmanned vehicles. This self-service capability eliminates the need for human delivery personnel to make judgment calls about appropriate drop-off spots, thereby increasing productivity and consistency while maintaining operational flexibility through automated adaptation to recipient preferences.
3Reliability
If precise target drop-off spot designation is implemented, then delivery security and control are improved, but the complexity of the delivery system increases
Solution Approach 1:
The patent implements preliminary action by having recipients designate their preferred drop-off spots in advance through interaction with virtual models before the actual delivery occurs. These designations are stored as target coordinates in the system. When the unmanned vehicle arrives at the delivery location, it simply needs to navigate to the pre-determined coordinates and execute the delivery, rather than requiring complex real-time decision-making or human intervention at the moment of delivery. This preliminary designation approach enhances delivery security and control while keeping the actual delivery operation simple.
Data Source
AI summary
An example method may include receiving, from a client computing device, an indication of a target drop-off spot for an object within a first virtual model of a first region of a delivery destination. A second virtual model of a second region of the delivery destination may be determined based on sensor data received from one or more sensors on a delivery vehicle. A mapping may be determined between physical features represented in the first virtual model and physical features represented in the second virtual model to determine an overlapping region between the first and second virtual models. A position of the target drop-off spot within the second virtual model may be determined based on the overlapping region. Based on the position of the target drop-off spot within the second virtual model, the delivery vehicle may be navigated to the target drop-off spot to drop off the object.