Clustered Flight Path Optimization for Aerial Gas Leak Detection
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Solution Overview
Problem
Current gas leak detection methods lack efficiency in optimizing flight paths for aerial vehicles to survey dispersed assets, such as oil and gas infrastructure, due to limitations in considering time, topology, wind conditions, and asset type, leading to suboptimal survey coverage and potential gaps or overlaps in asset inspection.
Innovation Solution
A method and system that divide an area into clusters based on spatial locations of assets, determining bounds and flight plans for aerial vehicles to survey each cluster efficiently, taking into account wind data, asset type, user preferences, and constraints like power level and cost functions, allowing for adjustments in cluster formation and sub-clustering for more comprehensive and efficient gas leak detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional gas leak detection methods are used without route optimization, then the survey process is simple to implement, but the survey coverage is incomplete and inspection efficiency is low
Solution Approach 1:
The patent divides the survey area into multiple clusters of assets, with each cluster processed by a separate aerial vehicle or flight segment. This segmentation allows parallel processing of different asset groups, improving overall survey efficiency while keeping individual flight paths manageable and less complex.
Solution Approach 2:
The system performs preliminary clustering of assets based on spatial locations before generating flight paths. By pre-organizing assets into clusters and determining optimal bounds beforehand, the system reduces the complexity of real-time route planning while ensuring comprehensive survey coverage.
2Measurement precision
If flight paths are optimized to cover all assets thoroughly, then detection accuracy improves, but flight time and energy consumption increase
Solution Approach 1:
The patent applies different survey parameters and flight path densities to different clusters based on their specific characteristics, such as asset type, proximity, and risk factors. This localized approach ensures high detection accuracy for critical assets while optimizing flight time by adjusting coverage intensity in different areas.
Solution Approach 2:
The system dynamically adjusts flight path parameters, such as altitude, speed, and spacing, based on cluster characteristics and environmental conditions like wind data. This allows the system to maintain detection accuracy while minimizing flight time and energy consumption by adapting parameters to local requirements.
3Productivity
If multiple aerial vehicles are deployed to survey dispersed assets simultaneously, then survey coverage improves, but coordination complexity and resource requirements increase
Solution Approach 1:
The patent assigns different clusters of assets to different aerial vehicles, creating independent survey zones. This segmentation enables parallel execution of surveys by multiple vehicles while reducing coordination complexity, as each vehicle operates autonomously within its assigned cluster with pre-determined flight paths.
Solution Approach 2:
The system pre-determines flight paths and launch platform locations for multiple aerial vehicles before deployment. By calculating optimal routes and bounds for each vehicle in advance, the system enables simultaneous operation with minimal real-time coordination, reducing operational complexity while maintaining comprehensive coverage.
4Reliability
If flight paths are planned without considering environmental factors like wind, then planning simplicity is maintained, but survey reliability and detection accuracy deteriorate
Solution Approach 1:
The patent incorporates environmental factors such as wind data into flight path planning by dynamically adjusting flight parameters like altitude, speed, and direction. This ensures survey reliability by compensating for environmental conditions while maintaining manageable planning complexity through automated parameter optimization.
Data Source
AI summary
Systems, devices, and methods including receiving, by a processor having addressable memory, a spatial location of one or more known assets; determining, by the processor, one or more clusters based on the received spatial location of the one or more known assets; determining, by the processor, a bound for each asset of the one or more known assets in each cluster; and determining, by the processor, a flight plan for an aerial vehicle for each cluster, where the flight plan surveys each asset in each cluster.