Federated multi-agent reinforcement learning for UAV collision avoidance in dense smart city environment
Unmanned Aerial Vehicles (UAVs) or drones are rapidly increasing in various industries, including agriculture, industrial applications, traffic and transportation management, surveillance, engineering, delivery, photography, and videography. Whether we like it or not, the rapid proliferation of...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/116475/ http://psasir.upm.edu.my/id/eprint/116475/1/116475.pdf |
| Summary: | Unmanned Aerial Vehicles (UAVs) or drones are
rapidly increasing in various industries, including agriculture,
industrial applications, traffic and transportation management,
surveillance, engineering, delivery, photography, and
videography. Whether we like it or not, the rapid proliferation
of drones with a wide variety of uses will define the immediate
smart cities. One of the most challenging issues caused by the
abundance of drones is controlling their air traffic to avoid
collisions. Multi-Agent Learning (MAL) may help drones avoid
collisions and make intelligent movements to prevent conflicts.
Most existing algorithms are limited to a small number of
drones; therefore, in this study, we trained drones in a congested
urban setting where a large number of drones were taught
concurrently using Federated Multi-Agent Learning. The
simulation results indicate that drones have a high success rate
in avoiding collisions with other drones and can avoid most
collisions. |
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