Drone Air Traffic Control Using 4D Cell Locking
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Solution Overview
Problem
Current drone management systems face challenges with air traffic control, particularly in high traffic volumes, congestion, and lack of infrastructure for flight plan management, collision avoidance, and safe operation over populated areas.
Innovation Solution
A scalable, flexible, automated air traffic control and flight plan management system that uses four-dimensional (4D) cells to construct and manage flight plans for drones, ensuring congestion reduction by locking exclusive air-space and rerouting if locks fail, leveraging existing cellular networks for communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional radio control protocols are used for drone management, then basic communication is achieved, but the system cannot handle high traffic volumes and congestion
Solution Approach 1:
The air space is segmented into discrete three-dimensional cells that can be individually locked and managed. This segmentation allows the system to handle multiple drones simultaneously by assigning specific spatial segments to each drone, thereby increasing traffic handling capacity while maintaining manageable system complexity through modular cell-based control.
Solution Approach 2:
The patent introduces a fourth dimension (time) to the traditional three-dimensional spatial cells, creating four-dimensional cells. This dimensional extension allows the system to manage traffic density by controlling not only spatial occupation but also temporal occupation of each cell, enabling high traffic volumes to be handled by distributing drones across both space and time dimensions.
2Reliability
If exclusive locks are placed on 4D cells to reduce congestion, then collision avoidance is improved, but flight plan execution may fail due to lock conflicts
Solution Approach 1:
The system performs preliminary locking of four-dimensional cells before drone execution of flight plans. By securing the necessary spatial and temporal cells in advance, the system ensures collision avoidance is established beforehand, while the preliminary nature of this action allows for proactive conflict detection and resolution before actual flight execution.
Solution Approach 2:
The system implements feedback mechanisms to monitor lock status and flight plan execution. When lock conflicts are detected or execution failures occur, the system provides feedback that triggers automatic rerouting to alternative cells. This feedback loop maintains high reliability by continuously adjusting flight paths based on real-time lock status and execution outcomes.
3Adaptability or versatility
If automated air traffic control is implemented, then scalability is improved, but system complexity increases
Solution Approach 1:
The automated air traffic control system operates autonomously without requiring human intervention for routine decisions. Drones automatically receive locked flight plans and execute them, while the system self-manages lock allocation and conflict resolution. This self-service approach enhances scalability by eliminating the bottleneck of human controllers while keeping complexity manageable through rule-based automated decision-making.
Solution Approach 2:
The system manages complexity by dynamically adjusting parameters such as cell dimensions, lock durations, and rerouting thresholds based on traffic conditions. These parameter changes allow the automated system to adapt to varying traffic volumes and complexity levels, maintaining scalability while preventing the system from becoming overwhelmingly complex under different operational conditions.
Data Source
AI summary
One embodiment provides a method comprising receiving a flight plan request for a drone. The flight plan request comprises a drone identity, departure information, and arrival information. The method further comprises constructing a modified flight plan for the drone based on the flight plan request, wherein the modified flight plan represents an approved, congestion reducing, and executable flight plan for the drone, and the modified flight plan comprises a sequence of four-dimensional (4D) cells representing a planned flight path for the drone. For each 4D cell of the modified flight plan, the method further comprises attempting to place an exclusive lock on behalf of the drone on the 4D cell, and in response to a failure to place the exclusive lock on behalf of the drone on the 4D cell, rerouting the modified flight plan around the 4D cell to a random neighboring 4D cell.