Drone Fleet Coordination for Faster Property Damage Data Collection
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Solution Overview
Problem
Current methods for collecting data on property damage, such as those used in insurance claims, are time-consuming, inefficient, and prone to inaccuracies due to manual control and potential collisions among multiple drones, leading to extended claim resolution times.
Innovation Solution
A system comprising a fleet of autonomous or semi-autonomous drones communicatively coupled with a damage identification computing device that navigates a geographical region to detect damage, reduces overlap in data collection, and stores aggregated data in a blockchain for secure record-keeping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single drone is used to collect damage data, then collision risk is reduced, but data collection time increases due to repeated repositioning
Solution Approach 1:
The system divides the geographical region into multiple zones and assigns different drones to specific zones, allowing parallel data collection without collisions. The damage identification computing device coordinates multiple drones to operate simultaneously in different areas, thereby reducing overall data collection time while maintaining safety through spatial segmentation.
Solution Approach 2:
The system transitions from single-drone sequential operation to multi-drone parallel operation by adding the dimension of spatial distribution. Multiple drones operate simultaneously at different locations within the geographical region, converting a time-consuming sequential process into a parallel process that reduces total collection time while maintaining collision avoidance through spatial separation.
2Loss of time
If multiple drones are used to capture damage data simultaneously, then data collection time is reduced, but collision risk and data overlap increase
Solution Approach 1:
The damage identification computing device receives real-time location and status data from multiple drones, processes this information, and sends back coordination commands to optimize flight paths and prevent collisions. This feedback loop enables dynamic adjustment of drone operations to maintain safe separation while maximizing parallel data collection efficiency.
Solution Approach 2:
The damage identification computing device acts as an intermediary that coordinates between multiple drones, managing their operations to prevent collisions and optimize data collection. It processes information from all drones and distributes optimized navigation instructions, enabling safe parallel operation without direct drone-to-drone communication.
3Ease of operation
If manual control is used for drone navigation, then flexibility is maintained, but data collection efficiency and accuracy decrease
Solution Approach 1:
The drones are equipped with autonomous navigation capabilities that allow them to independently navigate to optimal positions for damage data collection without continuous manual intervention. The system performs self-service by automatically positioning drones, capturing images, and transmitting data, significantly improving efficiency while maintaining operational flexibility through programmable control.
Solution Approach 2:
The system replaces manual mechanical control with automated electronic control and autonomous navigation systems. Drones use onboard sensors, processors, and communication systems to autonomously navigate and collect data, substituting human-operated mechanical control with automated systems that improve efficiency and accuracy while maintaining flexibility through software control.
Data Source
AI summary
A damage identification (DI) system for identifying property damage may include a drone fleet including several autonomous or semi-autonomous drones communicatively coupled together and a DI computing device. Each drone may collect drone-collected damage data, including image data. The DI computing may assign a geographical region to the drone fleet. The drone fleet may automatically navigate to, and then within, the geographical region to detect potential damage to properties. The DI computing device may further receive drone-collected damage data associated with a property within the geographical region from the drone fleet when the drone fleet determines the property is actually or potentially damaged, generate aggregated damage data associated with the property based at least partially upon the drone-collected damage data, and/or store the aggregated damage data in a blockchain structure associated with the property for damage assessment of the property.