Unmanned Aerial Vehicles (UAVs) are aircraft systems that operate remotely or autonomously using on-board computers without the need for a human pilot. This paper investigates the split control design of UAV swarms utilizing the master–slave paradigm, which improves UAV operations in complicated situations, including search and rescue missions, border monitoring, and disaster response. The proposed system ensures strong communication and steady flight formations by combining genetic algorithms with A* algorithms for effective trajectory planning. We specifically designed a new ZigBee-based communications protocol to address the unique challenges associated with UAV communications within FANETs (flying ad hoc networks). We cover the decentralized architecture of the FANET framework. Finally, we test the efficiency of our protocol by integrating a Raspberry Pi 3 Model B board with the XBEE PRO S3B 915 MHz module into a DJI Phantom 3 Standard UAV. Findings showed that the UAV and ground station successfully transmitted images across different test conditions while maintaining consistent performance levels.
The work uses stable formations and decentralized control to improve UAV swarm efficiency in difficult missions.
In UAV networks, a ZigBee-based communication protocol guarantees reliable data sharing under a variety of circumstances.
Optimized route planning with hybrid algorithms enhances obstacle navigation, energy efficiency, and UAV flight stability.
Decentralized control design for UAV swarms communication
Discov Appl Sci
Discover Applied Sciences ; 7 , 2
2025-02-10
Article (Journal)
Electronic Resource
English
Unmanned aerial vehicle , UAV swarms , Path tracing , Multi-target surveillance , Master–slave , Ardupilot , Swarm optimization Information and Computing Sciences , Artificial Intelligence and Image Processing , Engineering , Engineering, general , Materials Science, general , Earth Sciences, general , Applied and Technical Physics , Chemistry/Food Science, general , Environment, general
Decentralized probabilistic density control of autonomous swarms with safety constraints
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