Drone Task Reassignment Using Real-Time Delivery Value Prioritization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current drone management systems lack efficient methods for dynamically reassigning tasks and optimizing operations in a changing transportation environment, leading to suboptimal use of resources and potential delays.
Innovation Solution
Implement a mobility network server that coordinates aerial and ground vehicle operations, using unmanned aerial vehicle traffic management systems to dynamically assign tasks based on payload nature, proximity, charge capacities, and distances, allowing for real-time reevaluation and reassignment of tasks to prioritize higher-value deliveries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional static task assignment methods are used in drone management, then system simplicity is maintained, but operational efficiency and adaptability to changing conditions deteriorate
Solution Approach 1:
The patent implements dynamic task reassignment where the mobility network server continuously monitors drone status, payload characteristics, and environmental conditions to optimize task allocation in real-time. This allows the system to adapt to changing conditions such as drone charge levels, payload priorities, and delivery locations, thereby improving operational efficiency without requiring complete system redesign
Solution Approach 2:
The system employs feedback mechanisms where task performance data, drone status information, and delivery outcomes are continuously collected and used to refine future task assignments. The mobility network server uses this feedback to learn from past operations and improve assignment decisions, enhancing productivity while maintaining manageable system complexity through data-driven optimization
2Productivity
If real-time dynamic reassignment of tasks is implemented, then operational efficiency and throughput are improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments the task assignment problem into manageable components by evaluating individual task parameters (payload nature, delivery location, distance) and drone attributes (charge capacity, current position) separately before synthesizing optimal assignments. This modular approach allows real-time optimization of throughput while keeping computational complexity tractable through structured decision-making
Solution Approach 2:
The system optimizes throughput by dynamically changing key parameters such as task priority weights, drone selection criteria, and assignment thresholds based on current operational conditions. The mobility network server adjusts these parameters in real-time to maximize delivery throughput without requiring complex computational models, achieving high productivity through adaptive parameter tuning
3Reliability
If tasks are reassigned based on multiple factors (payload nature, proximity, charge capacities, distances), then task assignment quality improves, but processing time and system complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing key parameters such as drone charge capacities, delivery location coordinates, and payload characteristics before task assignment. The mobility network server prepares assignment criteria and evaluates potential assignments in advance, ensuring high task assignment quality while minimizing real-time processing time through proactive data preparation
Data Source
AI summary
Disclosed are embodiments for determining efficient utilization of drones. In some aspects, a drone may be performing a task, and a new task may be identified. Whether the drone should be diverted from the existing task to the new task, in some embodiments, is based on a number of factors. These factors include, for example, a value associated with the existing task and a value associated with the new task. The values are based on, for example, a potential delay introduced in completing the existing task if the drone is diverted to the new task.