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

Area of Science:

• Operations Research
• Robotics
• Disaster Management

Background:

• Post-disaster search and rescue operations face challenges in maximizing victim identification.
• Efficient routing of autonomous systems like drones is crucial for timely response.

Purpose of the Study:

• To address the Drone Swarms Routing Problem (DSRP) for enhanced victim identification in post-disaster scenarios.
• To propose and evaluate a cooperative drone swarm approach utilizing V-shaped formations with leader replacements.

Main Methods:

• Modeling the post-disaster area as a complete graph with vertices representing search locations and edges representing battery consumption.
• Developing a computational model where each drone acts as an agent employing the Drone Swarm Heuristic for location selection.
• Implementing and testing offline and online versions of the proposed method using scenarios based on the 2020 Beirut port explosion.

Main Results:

• The proposed Drone Swarm Heuristic demonstrated efficiency in maximizing victim identification.
• Numerical experiments validated the computational model's coverage capacity.
• The V-shaped formation with leader replacements proved advantageous for energy saving and search operations.

Conclusions:

• The developed drone swarm routing model effectively enhances search capabilities in post-disaster environments.
• Cooperative drone swarms with optimized formations offer a promising solution for improving disaster response efficiency.
• The study highlights the potential of intelligent routing algorithms for autonomous systems in critical situations.