Drone Flight Plan Optimization for Distributed Asset Maintenance
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Solution Overview
Problem
Current asset management practices rely on manual expert intervention for maintenance and repairs, which is costly and inefficient, and require dedicated instrumentation for monitoring assets, which can be expensive.
Innovation Solution
A computer-implemented method for drone deployment that identifies issues at assets, selects appropriate drones, generates initial and updated flight plans based on real-time data, predicts costs and time required, and coordinates overall flight plans for multiple drones to efficiently perform maintenance and repairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual expert intervention is used for asset maintenance and repair, then repair quality and reliability are ensured, but operational costs and time consumption increase
Solution Approach 1:
The system enables automated self-service through drone deployment. The optimization engine automatically generates flight plans, selects appropriate drones, and coordinates maintenance operations without requiring manual expert intervention for each asset, thereby improving operational efficiency while maintaining repair quality through automated decision-making algorithms
Solution Approach 2:
The patent replaces manual mechanical intervention with an automated digital system. The optimization engine substitutes human experts with algorithm-driven decision-making that processes asset data, generates flight plans, and coordinates drone operations, reducing operational costs and time consumption while maintaining reliability through systematic optimization
2Measurement precision
If dedicated instrumentation is deployed for monitoring assets, then monitoring precision and feedback quality improve, but system cost and complexity increase
Solution Approach 1:
The system employs multi-functional drones that can perform both monitoring and maintenance tasks. Instead of deploying dedicated instrumentation for each function, the same drone platform executes diverse operations including asset monitoring, inspection, and repair, thereby reducing system complexity and cost while maintaining monitoring precision through integrated sensor suites
Solution Approach 2:
The optimization engine acts as an intermediary that processes data from existing asset instrumentation and coordinates drone responses. Rather than requiring dedicated monitoring equipment for each asset, the system uses a centralized optimization engine to aggregate and analyze data from multiple sources, reducing instrumentation requirements while maintaining monitoring precision through intelligent data processing
3Adaptability or versatility
If multiple drones are deployed for distributed asset maintenance, then coverage area and response capability expand, but coordination complexity and computational requirements increase
Solution Approach 1:
The system segments the asset management portfolio into multiple zones or regions, each served by specific drones. The optimization engine divides the overall coordination task into smaller sub-tasks for individual drones, allowing parallel operation across multiple assets while reducing overall coordination complexity through hierarchical task allocation
Solution Approach 2:
The system implements dynamic coordination where flight plans and drone assignments are continuously optimized based on real-time conditions. The optimization engine adjusts drone routes, task assignments, and scheduling dynamically in response to changing asset needs, weather conditions, and drone availability, enabling flexible multi-drone operation while managing coordination complexity through adaptive algorithms
Data Source
AI summary
Provided are a computer-implemented method, a computer program product, and a computer system for drone deployment for distributed asset maintenance and repair. Embodiments identify a fix for a problem at an asset and identify a drone to perform the fix. Embodiments generate an initial flight plan that describes a drone flight path for the drone, and embodiments generate an updated flight plan for the drone by updating the drone flight path using real-time air traffic data, real-time road traffic data, and real-time drone flight path conditions obtained from one or more edge devices. Embodiments generate an overall flight plan for the drone and one or more other drones using a predicted cost and a predicted period of time for the updated drone flight path. Embodiments send a drone flight path from the overall flight plan to the drone with instructions to fix the problem.