UAV Pre-Staging and Reconfiguration for Faster Dispatch
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Solution Overview
Problem
Current unmanned aerial vehicle (UAV) delivery systems face inefficiencies due to the need for additional flight legs from UAV nests to item provider locations, leading to increased costs and delays, as UAVs are not pre-configured or pre-staged to meet demand variations in different geographic areas.
Innovation Solution
Implementing a system where UAVs are dynamically reconfigurable and pre-staged at various locations based on predicted demand, allowing them to be dispatched from nearby locations, reducing the need for additional flight legs and minimizing delays by being pre-configured with appropriate components for anticipated payload types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If UAVs are stored at a central nest location, then fleet management is simplified, but additional flight legs to item provider locations increase costs and delays
Solution Approach 1:
The patent divides the centralized UAV nest into multiple distributed pre-staging locations throughout the geographic area. Each pre-staging location serves as a local hub closer to item providers, eliminating the need for long flight legs from a central location while maintaining organized fleet management through the control system.
Solution Approach 2:
The control system predicts future demand for UAV transport tasks and proactively dispatches UAVs to pre-staging locations before actual delivery requests are made. This preliminary positioning ensures UAVs are already near item providers when needed, reducing delivery delays without requiring permanent physical presence at all locations.
2Speed
If UAVs are pre-staged at multiple locations, then delivery response time is reduced, but fleet coordination complexity increases
Solution Approach 1:
The control system continuously monitors demand patterns, UAV positions, and task completion status across all pre-staging locations. This feedback enables dynamic redistribution of UAVs based on real-time conditions, coordinating the distributed fleet efficiently while maintaining fast response times through data-driven decision-making.
3Adaptability or versatility
If UAVs are physically reconfigurable, then adaptability to different payload types is improved, but configuration time and complexity increase
Solution Approach 1:
The UAVs are designed with physically reconfigurable components that allow a single UAV platform to perform multiple payload types. Standardized interfaces and modular components enable different configurations (e.g., passenger seats, cargo compartments, specialized equipment) to be attached to the same base UAV, achieving versatility without requiring entirely different vehicle designs.
Solution Approach 2:
The control system predicts the types of transport tasks and required payload configurations in advance. Based on these predictions, UAVs are pre-configured with appropriate components before being dispatched to pre-staging locations, minimizing configuration time when actual delivery requests are received.
Data Source
AI summary
An example method involves determining an expected demand level for a first type of a plurality of types of transport tasks for unmanned aerial vehicles (UAVs), the first type of transport tasks associated with a first payload type. Each of the UAVs is physically reconfigurable between at least a first and a second configuration corresponding to the first payload type and a second payload type, respectively. The method also involves determining based on the expected demand level for the first type of transport tasks, (i) a first number of UAVs having the first configuration and (ii) a second number of UAVs having the second configuration. The method further involves, at or near a time corresponding to the expected demand level, providing one or more UAVs to perform the transport tasks, including at least the first number of UAVs.