Drone Air Traffic Control System Using Heuristic Weather Probing
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Solution Overview
Problem
Current drone management systems face challenges such as lack of general air traffic control, scalability issues, congestion in heavily congested airspace, and inadequate infrastructure for flight plan management, weather forecasts, and fail-safe designs for safe operation over populated areas.
Innovation Solution
A scalable, flexible, automated air traffic control and flight plan management system that maintains a weather model for air traffic control zones, uses a hash table with spatially defined hash entries for predicting weather conditions, and generates executable flight plans by heuristically probing cells along a drone's planned path to determine feasibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional radio control protocols are used for drone management, then basic control functionality is maintained, but scalability to high traffic volumes and congestion is not achieved
Solution Approach 1:
The air traffic control zone is divided into a grid of cells, with each cell independently managed and tracked. This segmentation allows the system to handle high traffic volumes by distributing control across multiple discrete units rather than managing airspace as a single complex volume.
Solution Approach 2:
The patent introduces a fourth dimension (time) to the traditional three-dimensional airspace model, creating four-dimensional cells. This temporal dimension enables the system to manage traffic flow dynamically, allowing drones to occupy specific cells at specific times, thereby preventing congestion and enabling scalable traffic handling.
2Adaptability or versatility
If human controllers are used for air traffic control, then flexibility in handling complex situations is improved, but scalability to high traffic volumes deteriorates
Solution Approach 1:
Drones are equipped with onboard collision avoidance systems and autonomous navigation capabilities that allow them to self-manage their flight paths and avoid collisions without constant human intervention. This self-service approach enables the system to handle high traffic volumes while maintaining safety through automated decision-making at the drone level.
Solution Approach 2:
The system implements continuous feedback loops where drones report their status, position, and environmental conditions to the air traffic control system, which then adjusts flight plans and provides guidance. This automated feedback mechanism maintains adaptability while enabling scalable traffic management through real-time data-driven decisions.
3Measurement precision
If comprehensive weather monitoring for all cells is implemented, then flight plan feasibility accuracy is improved, but computational resources and time required deteriorates
Solution Approach 1:
Instead of comprehensively monitoring weather conditions in all air traffic control cells, the system selectively probes only those cells that are relevant to the planned flight path. This partial action approach maintains sufficient weather awareness for decision-making while significantly reducing the computational resources and time required compared to complete coverage.
Solution Approach 2:
The system performs heuristic probing of weather conditions for cells along the planned flight path before finalizing the flight plan. By obtaining weather information in advance for relevant areas, the system can assess feasibility and make informed decisions without requiring exhaustive real-time weather data for the entire airspace.
Data Source
AI summary
One embodiment provides a method comprising maintaining a weather model based on predicted weather conditions for an air traffic control zone. A hash table comprising multiple hash entries is maintained. Each hash entry comprises a timestamped predicted weather condition for a cell in the zone. A flight plan request for a drone is received. The request comprises a planned flight path for the drone. For at least one cell on the planned flight path, same latitude or same longitude cells, whichever is most closely orthogonal to a direction of the planned flight path, are heuristically probed. Weather conditions for the at least one cell are estimated based on predicted weather conditions for the same latitude or same longitude cells. An executable flight plan is generated if the planned flight path is feasible based on the estimated weather conditions; otherwise, a report including an explanation of infeasibility is generated instead.