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Asymptotically Optimal Deployment of Drones for Surveillance and Monitoring
Andrey V Savkin1, Hailong Huang2
1School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney 2052, Australia. a.savkin@unsw.edu.au.
Sensors (Basel, Switzerland)
|May 7, 2019
Summary
This study presents an efficient algorithm for drone surveillance, determining the minimum number of drones needed to cover a ground area. The algorithm is proven to be asymptotically optimal for large regions.
Area of Science:
- Operations Research
- Computer Science
- Robotics
Background:
- Effective surveillance requires optimal drone deployment.
- Determining the minimum number of drones for area coverage is a complex challenge.
Purpose of the Study:
- To develop an easily implementable algorithm for estimating the minimum number of drones for surveillance.
- To determine optimal drone placement for comprehensive ground monitoring.
Main Methods:
- Development of a novel algorithm for drone placement and count estimation.
- Application of Kershner's theorem from combinatorial geometry for theoretical analysis.
- Comparative analysis with existing surveillance methods.
Main Results:
- An efficient algorithm for estimating the minimum number of surveillance drones.
- Proof of asymptotic optimality for the developed algorithm as region size increases.
- Demonstrated efficiency through illustrative examples and comparisons.
Conclusions:
- The developed algorithm provides an effective solution for drone surveillance problems.
- The algorithm's asymptotic optimality ensures scalability for large-area monitoring.
- The method offers a practical and efficient approach to drone deployment optimization.
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