Aerial Vehicle Fleet Control for Probabilistic Service Coverage
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Solution Overview
Problem
Conventional methods struggle to provide reliable and consistent data connectivity in areas where conventional ground infrastructure is difficult to install, particularly due to unpredictable wind and meteorological forces affecting lighter-than-air vehicles used for communication services.
Innovation Solution
A system and method for controlling a group of aerial vehicles to meet connectivity service objectives by determining the probability of service coverage and adjusting their flight policies, including station seeking, to ensure desired coverage levels, using simulations and probabilistic approaches based on wind patterns and vehicle characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If lighter-than-air vehicles are used for connectivity services, then deployment flexibility and ease of installation are improved, but reliability of service delivery deteriorates due to unpredictable wind and meteorological forces
Solution Approach 1:
The patent implements dynamic flight policies that adapt to real-time wind conditions and vehicle states. The system transitions between different operational modes (station seeking, loitering, transit) based on probabilistic assessments of coverage requirements and environmental conditions, allowing the fleet to dynamically respond to unpredictable weather while maintaining service reliability
Solution Approach 2:
The system employs continuous feedback loops where the probabilistic coverage calculator monitors vehicle positions, wind patterns, and service area coverage in real-time. This feedback informs the flight policy adjustments, creating a closed-loop control system that maintains reliability despite environmental uncertainties
2Manufacturing precision
If detailed minute-by-minute flight planning is implemented, then trajectory precision is improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent applies partial planning by focusing computational resources on critical decision points (mode transitions, station seeking entries) rather than continuous minute-by-minute scheduling. The system determines vehicle destinations and operational modes at key moments, accepting probabilistic outcomes for intermediate periods, thereby reducing complexity while maintaining sufficient precision for service delivery
3Reliability
If the number of aerial vehicles is increased to ensure coverage, then service coverage probability is improved, but operational cost and fleet management complexity increase
Solution Approach 1:
The system optimizes the fleet composition by varying parameters such as vehicle altitude, horizontal position, and operational mode rather than simply increasing the number of vehicles. The probabilistic coverage calculator assesses how different parameter configurations contribute to overall coverage, enabling efficient utilization of existing fleet resources to achieve desired coverage probabilities
Data Source
AI summary
Methods and systems for controlling a group of aerial vehicles to meet a connectivity service objective are provided. A method may include causing aerial vehicles in the group to arrive at a target service area during a given arrival time window associated with the connectivity service objective, which may indicate a desired probability of service coverage of the target service area. The method further includes calculating a probability of service coverage of the target service area for the vehicles for a time period after the vehicles are expected to arrive at the target service area, determining whether the probability of service coverage meets a threshold, and causing the vehicles to operate according to a station seeking flight policy during the time period.