Drone Surveillance Reassignment for Continuous Area Coverage
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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 compromised due to weather, geography, hardware failure, or need for recharging, without operator intervention.
Innovation Solution
A surveillance method that dynamically adjusts the number of sub-areas based on the operational drones, using real-time segmentation and clustering of virtual points in a georeferenced area, employing k-means clustering and Voronoi diagrams to ensure all areas are covered without overlap.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the area of interest is divided into fixed sub-areas assigned to each drone, then the surveillance coverage is initially complete, but gaps in surveillance occur when drones become compromised or need recharging
Solution Approach 1:
The patent implements dynamic reassignment of surveillance sub-areas based on real-time operational status of drones. When a drone becomes compromised or needs recharging, the system automatically recalculates and redistributes its sub-area to remaining operational drones, ensuring continuous surveillance coverage without fixed assignments.
Solution Approach 2:
The system continuously monitors the operational status of drones and uses this feedback to trigger reassignment operations. When a drone's operational status changes (becomes compromised or needs recharging), the system detects this through feedback mechanisms and automatically initiates reassignment of sub-areas to maintain complete surveillance coverage.
2Ease of operation
If manual operator intervention is used to reassign drones to sub-areas, then flexibility in handling operational changes is achieved, but response time increases and continuous coverage may be compromised
Solution Approach 1:
The system implements self-service automation where the surveillance management system automatically detects when a drone becomes compromised or needs recharging, calculates optimal reassignment of sub-areas, and executes the reassignment without requiring manual operator intervention. This maintains continuous surveillance coverage while eliminating response delays associated with manual control.
3Device complexity
If equal-sized sub-areas are assigned to each drone, then initial distribution is simple and balanced, but inefficient use of resources occurs when the number of operational drones changes
Solution Approach 1:
The system transitions from static equal-sized sub-area assignments to dynamic reassignment based on the number of operational drones. When drones become compromised or need recharging, the system automatically recalculates sub-area boundaries and redistributes them among remaining operational drones, optimizing surveillance efficiency while maintaining manageable complexity through automated algorithms.
Data Source
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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 comprising 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 subareas, N being the current number of operational drones; and - an affectation step for affecting each operational drone to a respective sub-area for surveilling said sub-area.