Fleet Vehicle Control Policies for Coverage Optimization
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Solution Overview
Problem
In areas where data connectivity is limited or unreliable, there is a need for additional network infrastructure to ensure consistent and efficient data coverage, particularly in regions with varying demand for internet and cellular data networks.
Innovation Solution
A method and system that determine control policies for a fleet of vehicles, such as high-altitude balloons, to optimize their distribution and trajectories within a region based on coverage requirements, using stochastic optimization to ensure alignment with desired distributions and minimize discrepancies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional network infrastructure is deployed to improve data connectivity coverage, then coverage reliability is improved, but system complexity and deployment cost increase
Solution Approach 1:
The fleet of vehicles autonomously navigates and positions itself to satisfy coverage requirements without continuous human intervention. The system receives coverage requirements, determines control policies for each vehicle, and executes trajectories automatically, allowing the infrastructure to self-manage its deployment and optimization.
Solution Approach 2:
The system dynamically adjusts vehicle trajectories and positions based on time-varying coverage requirements. Control policies are determined for each vehicle to satisfy a sequence of coverage requirements at different phases within a period of time, enabling adaptive response to changing connectivity demands.
2Reliability
If fleet distribution is optimized to meet coverage requirements, then network reliability is improved, but computational complexity increases
Solution Approach 1:
The system divides the fleet into individual vehicles, each with its own control policy. The overall coverage problem is segmented into individual vehicle trajectory planning, where each vehicle's control policy is determined based on its initial location and the sequence of coverage requirements, making the complex optimization problem more manageable.
Solution Approach 2:
The system changes the parameter representation from continuous spatial optimization to discrete control policies for each vehicle. By representing control policies as sequences of actions for each vehicle at different time phases, the system transforms the optimization problem into a parameter-based formulation that can be scored and compared.
3Manufacturing precision
If vehicle trajectories are precisely controlled to satisfy coverage requirements, then coverage precision is improved, but energy consumption increases
Solution Approach 1:
The system determines control policies that satisfy coverage requirements at specific phases within a period of time, rather than requiring continuous precise control. This allows vehicles to maintain positions that meet coverage thresholds without excessive energy expenditure on constant adjustment.
Solution Approach 2:
The system determines all control policies and trajectories in advance for the entire period of time, rather than making continuous real-time adjustments. By pre-planning the sequence of control actions for each vehicle, the system achieves precise coverage while minimizing energy consumption through efficient, pre-optimized paths.
Data Source
AI summary
Methods and systems for determining control policies for a fleet of vehicles are provided. In one example, a method is provided that comprises receiving a sequence of coverage requirements for a region and an associated period of time, and receiving an initial location of one or more vehicles of a fleet of vehicles. The method may further include determining a control policy for each of the one or more vehicles. Additionally, based on the determined control policies and the initial locations, one or more estimated distributions of the fleet of vehicles at respective phases within the period of time may be determined. According to the method, a score associated with the control policies may be determined based on a comparison between the estimated distributions and corresponding desired distributions of the sequence of coverage requirements. In some examples, the control policies may also be revised using an optimization technique.