Drone Traffic Cell Scheduling for Congested Urban Airspace
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Solution Overview
Problem
Current drone management systems face challenges such as lack of air traffic control infrastructure, congestion handling, and safety features for drones in populated areas, particularly in high-traffic volumes and complex airspace scenarios.
Innovation Solution
A scalable, flexible, automated air traffic control and flight plan management system that partitions air-space into fine-grained 4D cells, using existing cellular networks for communication, and schedules drone tasks to avoid congestion by locking exclusive 4D cells, ensuring safe and efficient drone operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional radio control protocols are used for drone management, then current systems can operate drones, but they cannot handle high traffic volumes and congestion in heavily populated areas
Solution Approach 1:
The system segments the airspace into discrete three-dimensional cells, with each cell representing a specific geographic volume at a particular altitude. This segmentation allows individual drones to be assigned to specific cells, enabling systematic management of high traffic volumes while maintaining safety through structured spatial organization and collision avoidance.
2Extent of automation
If automated flight plan management is implemented, then scalability is improved, but complexity of the control system increases
Solution Approach 1:
The system enables drones to autonomously file flight plans, receive cell assignments, and execute missions independently. The automated air traffic control system processes these requests through standardized protocols, reducing the need for human intervention while managing system complexity through algorithmic automation rather than complex infrastructure.
3Adaptability or versatility
If drones operate in heavily populated areas, then service coverage is expanded, but collision risks and safety hazards increase
Solution Approach 1:
The system utilizes three-dimensional cells that incorporate vertical altitude as a distinct dimension, allowing drones to operate at different heights over the same geographic area. This dimensional approach enables expanded service coverage over populated areas while maintaining safety by vertically separating drone traffic paths and preventing horizontal collisions.
4Reliability
If fine-grained 4D cell partitioning is used, then congestion is reduced and collision risks decrease, but computational requirements and system complexity increase
Solution Approach 1:
The system replaces complex mechanical air traffic control infrastructure with a computational framework that uses standardized protocols and algorithms to manage drone traffic. The fine-grained four-dimensional cell partitioning (three spatial dimensions plus time) is handled through software-based assignment and tracking, reducing physical system complexity while maintaining high reliability for congestion and collision avoidance.
Data Source
AI summary
Embodiments of the present invention provide a method comprising receiving a task set comprising multiple tasks, receiving operational information identifying one or more operating characteristics of multiple drones, and obtaining an initial heuristic ordering of the multiple tasks based on the operational information and the climate information. Each task has a corresponding task location. The method further comprises scheduling the multiple tasks to obtain a final ordering of the multiple tasks. The final ordering represents an order in which the multiple tasks are scheduled, and the final ordering may be different from the initial heuristic ordering.