Intelligent robot swarms are increasingly being explored as tools for search and rescue missions. Efficient path planning and robust communication networks are critical elements of completing missions. The focus of this research is to give unmanned aerial vehicles (UAVs) the ability to self-organize a mesh network that is optimized for area coverage. The UAVs will be able to read the communication strength between themselves and all the UAVs it is connected to using RSSI. The UAVs should be able to adjust their positioning closer to other UAVs if RSSI is below a threshold, and they should also maintain communication as a group if they move together along a search path. Our approach was to use Genetic Algorithms in a simulated environment to achieve multi-node exploration with emphasis on connectivity and swarm spread.



    title={Area-Optimized UAV Swarm Network for Search and Rescue Operations},
    author={Laik Ruetten, Paulo Alexandre Regis, David Feil-Seifer, and Shamik Sengupta},
    address={Las Vegas, NV, USA},
    booktitle={2020 10th Annual Computing and Communication Workshop and Conference (CCWC)},