Drone Surveillance Segmentation for Coverage After Fleet Changes
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Solution Overview
Problem
Existing drone surveillance methods fail to maintain continuous coverage of an area of interest when some drones become operational, due to weather, geography, hardware malfunctions, or the need for recharging, leading to gaps in surveillance without operator intervention.
Innovation Solution
A surveillance method that dynamically segments the area of interest into sub-areas based on the number of operational drones, using a control unit to monitor drone availability and reassign drones to ensure continuous coverage through georeferenced virtual points clustering and Voronoi diagram generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed number of drones are assigned to fixed sub-areas, then the system structure is simple and easy to operate, but the surveillance coverage becomes discontinuous when drones become non-operational
Solution Approach 1:
The patent implements dynamic reassignment of drones to sub-areas based on real-time operational status. When a drone becomes non-operational, the system automatically detects the failure and reassigns other operational drones to cover the affected sub-areas, transforming the static assignment model into a dynamic adaptive model that maintains continuous surveillance coverage.
Solution Approach 2:
The system performs self-diagnosis and self-reassignment when drone failures occur. The control unit automatically detects non-operational drones and reassigns sub-areas without requiring operator intervention, enabling the surveillance system to self-heal and maintain continuous coverage through autonomous decision-making.
2Speed
If manual operator intervention is used to reassign drones, then the reassignment logic can be complex and adaptive, but the response time is delayed and operator burden increases
Solution Approach 1:
The control unit automatically detects when drones become non-operational and autonomously performs reassignment of sub-areas without requiring operator intervention. This self-service mechanism enables immediate response to failures while reducing operator burden, as the system handles the entire detection and reassignment process autonomously.
Solution Approach 2:
The system continuously monitors drone operational status and uses this feedback to trigger automatic reassignment when failures are detected. The control unit receives real-time status information from drones and automatically adjusts sub-area assignments based on current operational conditions, creating a closed-loop control system that responds immediately to changes.
3Reliability
If drones are reassigned dynamically based on operational status, then continuous surveillance coverage is maintained, but the system complexity and computational requirements increase
Solution Approach 1:
The surveillance area is divided into multiple discrete sub-areas that can be independently assigned to different drones. This segmentation allows the system to manage complexity by treating each sub-area as an independent unit that can be reassigned without affecting the entire system, enabling modular and scalable implementation of dynamic reassignment.
Solution Approach 2:
The system changes the assignment parameters (which drone covers which sub-area) dynamically based on operational status. When drone operational status changes, the control unit modifies the assignment parameters to maintain optimal coverage, allowing the system to adapt to failures while maintaining manageable complexity through parameter-based control.
Data Source
AI summary
The invention relates to a computer-implemented surveillance method for surveilling an area of interest using a set of drones, the surveillance method including monitoring a number of operational drones among the set of drones. If a change in the number of operational drones is detected, performing a segmentation step for segmenting the area of interest into N sub-areas, N being the current number of operational drones; and performing an affectation step for affecting each operational drone to a respective sub-area for surveilling said sub-area.