A method to maximize the total coverage of multiple unmanned aerial vehicles (UAVs) which monitor a bounded space is presented. The goal of all UAVs is to maximize their individual coverage while minimize possible coverage overlaps among them. This goal is achieved using a multi-agent reinforcement learning (MARL) method which is embedded with a coordination strategy that allows several UAVs to negotiate their actions to avoid possible overlaps between their coverage. Simulation results are shown to illustrate the developed MARL scheme's performance.
Area Coverage Maximization of Multi UAVs Using Multi-Agent Reinforcement Learning
14.12.2023
547795 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Reinforcement Learning Based Coverage Planning for UAVs Fleets
TIBKAT | 2023
|Multi-Agent Dynamic Area Coverage Based on Reinforcement Learning with Connected Agents
BASE | 2023
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