Human–Machine Network Through Bio-Inspired Decentralized Swarm Intelligence and Heterogeneous Teaming in SAR Operations

Disaster management has always been a struggle due to unpredictable changing conditions and chaotic occurrences that require real-time adaption. Highly optimized missions and robust systems mitigate uncertainty effects and improve notoriously success rates. This paper brings a niching hybrid human–m...

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Vydáno v:Journal of intelligent & robotic systems Ročník 105; číslo 4; s. 88
Hlavní autoři: Longa, Marc Espinós, Tsourdos, Antonios, Inalhan, Gokhan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.08.2022
Springer
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Bees
Communication
Control
Data transmission
Decision making
Disaster management
Earthquakes
Electrical Engineering
Emergency management
Engineering
Heterogeneity
Honeybee
Hybrid systems
Intelligence
Markov analysis
Mechanical Engineering
Mechatronics
Monte Carlo method
Optimization
Regular Paper
Robotics
Robustness
Search and rescue operations
Sensors
Simulation
Space exploration
Swarm intelligence
Teams
Decentralized Heterogeneous Systems
Bio-inspiration
Human–Machine Network
Monte Carlo Simulations
Safe and Rescue
Swarm Intelligence
ISSN:0921-0296, 1573-0409
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Shrnutí:Disaster management has always been a struggle due to unpredictable changing conditions and chaotic occurrences that require real-time adaption. Highly optimized missions and robust systems mitigate uncertainty effects and improve notoriously success rates. This paper brings a niching hybrid human–machine system that combines UAVs fast responsiveness with two robust, decentralized, and scalable bio-inspired techniques. Cloud-Sharing Network (CSN) and Pseudo-Central Network (PCN), based on Bacterial and Honeybee behaviors, are presented, and applied to Safe and Rescue (SAR) operations. A post-earthquake scenario is proposed, where a heterogeneous fleet of UAVs cooperates with human rescue teams to detect and locate victims distributed along the map. Monte Carlo simulations are carried out to test both approaches through state-of-the-art metrics. This paper introduces two hybrid and bio-inspired schemes to deal with critical scouting stages, poor communications environments and high uncertainly levels in disaster release operations. Role heterogeneity, path optimization and hive data-sharing structure give PCN an efficient performance as far as task allocation and communications are concerned. Cloud-sharing network gains strength when the allocated agents per victim and square meter is high, allowing fast data transmission. Potential applications of these algorithms are not only comprehended in SAR field, but also in surveillance, geophysical mapping, security and planetary exploration.
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ISSN:0921-0296
1573-0409
DOI:10.1007/s10846-022-01690-5