Mission Preparation for Self-Organizing UAV Swarms on Multiuser Platform

Unmanned aerial vehicle (UAV) swarms offer a resource-effective and time-efficient method of collecting and analyzing data for a range of applications. The paper presents a cutting-edge collective artificial intelligence-powered self-organizing UAV swarm mission planning and preparing platform designed to enhance task performance with a fleet of UAVs and facilitate terrain monitoring. Users can create requests easily from the QT interface due to the cloud-based multiuser platform’s interactive capabilities, which enable smooth user collaboration and real-time video viewing for group study of dynamic landscape imagery. Optimizing the behavior and performance of UAV swarm navigation maps, the UAV map configurator makes it easier to create and modify them. Additionally, the QT service layer guarantees safe data transfer to cloud servers, and the parameter gossip system encourages coordination and communication among swarm members. This integrated data sets significant parameters such as the number of swarm participants, starting relative coordinates, and statuses (imager and/or strike), and drives the establishment of critical swarm and target jobs. The server uses complex algorithms, such as the research road graph, which is based on the rotor-router model, and the complete information exchange graph, which uses the gossip/broadcast model, to carry out these tasks. These algorithms complement each other in the server environment, allowing the UAV swarm to plan and coordinate tasks more effectively. In addition, the platform provides the smooth transfer of the created goal tasks to each member of the swarms’ memory, benefiting decision-making skills and swarm performance as an entire. The QT-based user interface also provides the capability to modify the specific member task type of UAVs. This implies that UAVs can be used for specific area consideration, information gathering, transport, or, in the case of a military use, as an enemy position attacker. A server environment and QT-based interface can be used as a data sharing transponder to accomplish all of these functional changes. The task categories within the mentioned swarm shift when alterations occur in the initial transfer graph of UAVs, necessitating the inclusion of new locations for completing the modified tasks. Additionally, ensuring a unified primary administrative domain and coordinated data transmission among users across multiple platforms are crucial aspects of the suggested method. This domain ensures secure connections and offers additional security features.

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Publication Details

Journal
Programming and Computer Software
Published
2024-10-01
DOI
https://doi.org/10.1134/s0361768824700397
Citations
3
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
1.05
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article

Mission Preparation for Self-Organizing UAV Swarms on Multiuser Platform

Vahagn Poghosyan, Suren Poghosyan, Hrachya V. Astsatryan, D. Hayrapetyan et al.
3 citations
Programming and Computer Software
Distributed Control Multi-Agent Systems
1.05
article

Mission Preparation for Self-Organizing UAV Swarms on Multiuser Platform

Vahagn Poghosyan, Suren Poghosyan, Hrachya V. Astsatryan, D. Hayrapetyan, Yuri Shoukourian, Artyom Lazyan, Agit Atashyan
article en
3 citations

Abstract

Unmanned aerial vehicle (UAV) swarms offer a resource-effective and time-efficient method of collecting and analyzing data for a range of applications. The paper presents a cutting-edge collective artificial intelligence-powered self-organizing UAV swarm mission planning and preparing platform designed to enhance task performance with a fleet of UAVs and facilitate terrain monitoring. Users can create requests easily from the QT interface due to the cloud-based multiuser platform’s interactive capabilities, which enable smooth user collaboration and real-time video viewing for group study of dynamic landscape imagery. Optimizing the behavior and performance of UAV swarm navigation maps, the UAV map configurator makes it easier to create and modify them. Additionally, the QT service layer guarantees safe data transfer to cloud servers, and the parameter gossip system encourages coordination and communication among swarm members. This integrated data sets significant parameters such as the number of swarm participants, starting relative coordinates, and statuses (imager and/or strike), and drives the establishment of critical swarm and target jobs. The server uses complex algorithms, such as the research road graph, which is based on the rotor-router model, and the complete information exchange graph, which uses the gossip/broadcast model, to carry out these tasks. These algorithms complement each other in the server environment, allowing the UAV swarm to plan and coordinate tasks more effectively. In addition, the platform provides the smooth transfer of the created goal tasks to each member of the swarms’ memory, benefiting decision-making skills and swarm performance as an entire. The QT-based user interface also provides the capability to modify the specific member task type of UAVs. This implies that UAVs can be used for specific area consideration, information gathering, transport, or, in the case of a military use, as an enemy position attacker. A server environment and QT-based interface can be used as a data sharing transponder to accomplish all of these functional changes. The task categories within the mentioned swarm shift when alterations occur in the initial transfer graph of UAVs, necessitating the inclusion of new locations for completing the modified tasks. Additionally, ensuring a unified primary administrative domain and coordinated data transmission among users across multiple platforms are crucial aspects of the suggested method. This domain ensures secure connections and offers additional security features.

Programming and Computer SoftwareVol. 50(S1)
Institute for Informatics and Automation Problems (AM)
Openalex Percentile: Top 21%
Distributed Control Multi-Agent Systems
1.05
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