Autonomous Delivery Navigation Using GPS and Identifier Scanning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current GPS-based drone delivery systems lack the accuracy and safety features needed for precise delivery in urban areas, as they cannot differentiate between close proximity delivery locations or account for obstructions, and existing high-accuracy navigation systems are expensive and power-intensive, making them unsuitable for commercial use.
Innovation Solution
A system utilizing machine-readable unique identifiers, such as barcodes or RFID tags, that allow autonomous delivery vehicles to precisely locate and deliver payloads by scanning these identifiers using a smartphone app, which associates the identifier with GPS location and stores this information on a server for navigation, enabling accurate and safe delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS-based navigation is used for drone delivery, then the system is cost-effective and simple to operate, but the delivery accuracy is insufficient to differentiate between close proximity locations
Solution Approach 1:
The navigation system is segmented into two functional parts: GPS for coarse location navigation and machine-readable identifiers for fine precision location identification. This segmentation allows the system to achieve high delivery accuracy without requiring the entire navigation system to be complex and expensive.
Solution Approach 2:
Machine-readable identifiers serve as an intermediary between the GPS system and the final delivery location. The identifier acts as a mediator that translates approximate GPS coordinates into precise delivery points, enabling accurate delivery without direct reliance on high-precision GPS alone.
2Measurement precision
If high accuracy navigation systems are used, then delivery precision is improved, but the cost and power consumption increase significantly
Solution Approach 1:
The navigation function is divided between GPS (low power) and machine-readable identifiers (minimal power). This segmentation enables the drone to achieve high delivery accuracy without continuously operating high-power navigation systems, thus reducing overall power consumption.
Solution Approach 2:
The machine-readable identifiers are pre-placed at delivery locations before the drone arrives. This preliminary action eliminates the need for the drone to perform complex real-time calculations or use high-power sensors during the delivery process, thereby reducing power consumption.
3Measurement precision
If machine-readable unique identifiers are used for precise location, then delivery accuracy is improved without high cost, but the system requires infrastructure setup and scanning capability
Solution Approach 1:
Instead of using complex physical markers or electronic displays, the system uses simple printed machine-readable identifiers that can be reproduced using standard printing technology. This copying approach maintains delivery accuracy while significantly reducing implementation complexity and cost.
Solution Approach 2:
The machine-readable identifiers are implemented as inexpensive printed materials that can be easily deployed and replaced if needed. This approach prioritizes low cost and ease of implementation over durability, allowing widespread deployment without significant infrastructure investment.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A system for delivery of payload at a precise location by autonomous delivery vehicle. A machine-readable unique identifier is laid at a place where a user wants delivery of an item. User opens a precise delivery app on smartphone, activates the scanner and standing near the unique identifier scans it. Precise delivery app reads the unique identity of the unique identifier and collects the geophysical location of the smartphone. Third party system feeds this information of the target unique identifier to the autonomous vehicle. The autonomous delivery vehicle includes a first prior art navigator and a second scanner navigator. The autonomous vehicle determines its route to the approximate location of the target unique identifier with the help of the first prior art navigator and the second scanner navigator scans every unique identifier that may be present around that location and guides the autonomous vehicle to the target unique identifier.