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. 2023 Nov 30;23(23):9540.
doi: 10.3390/s23239540.

Intelligent Drone Swarms to Search for Victims in Post-Disaster Areas

Matheus Nohra Haddad  1   2 , Andréa Cynthia Santos  1 , Christophe Duhamel  1 , Amadeu Almeida Coco  1
Affiliations

Intelligent Drone Swarms to Search for Victims in Post-Disaster Areas

Matheus Nohra Haddad et al. Sensors (Basel). .

Abstract

This study presents the Drone Swarms Routing Problem (DSRP), which consists of identifying the maximum number of victims in post-disaster areas. The post-disaster area is modeled in a complete graph, where each search location is represented by a vertex, and the edges are the shortest paths between destinations, with an associated weight, corresponding to the battery consumption to fly to a location. In addition, in the DSRP addressed here, a set of drones are deployed in a cooperative drone swarms approach to boost the search. In this context, a V-shaped formation is applied with leader replacements, which allows energy saving. We propose a computation model for the DSRP that considers each drone as an agent that selects the next search location to visit through a simple and efficient method, the Drone Swarm Heuristic. In order to evaluate the proposed model, scenarios based on the Beirut port explosion in 2020 are used. Numerical experiments are presented in the offline and online versions of the proposed method. The results from such scenarios showed the efficiency of the proposed approach, attesting not only the coverage capacity of the computational model but also the advantage of adopting the V-shaped formation flight with leader replacements.

Keywords: disaster relief; drone swarms; humanitarian logistics; multi-agents systems; routing.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Example of a DSRP with eight drones, one base and eight search areas.
Figure 2
Figure 2
Flowchart of the computational model for the DSRP.
Figure 3
Figure 3
How to build the heatmap and transform it into a complete graph.
Figure 4
Figure 4
Case study: Beirut Port explosion.
Figure 5
Figure 5
Example of a solution loaded in CoppeliaSim.
Figure 6
Figure 6
Scenario 1: Results varying the number of drones, in terms of time and percentage of expected victims.
Figure 7
Figure 7
Scenario 2: Results varying the number of drones, in terms of time and percentage of expected victims.
Figure 8
Figure 8
Scenario 3: Results varying the number of drones, in terms of time and percentage of expected victims.
Figure 9
Figure 9
Additional cost required without V-shaped formation savings.
Figure 10
Figure 10
Number of extra expected victims identified with V-shaped formation savings.
See this image and copyright information in PMC

References

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