Artificial intelligence (AI) based flight control algorithms can be successfully utilized to deploy a swarm of autonomous Unmanned Areal Vehicles (UAVs). In a swarm of autonomous UAVs operating as a mobile ad-hoc network (MANET), use of centralized control, pre-planned missions, synchronization of nodes and reliance on conditional procedures are not feasible We introduce near real-time AI based flight control algorithms for autonomous UAVs to position themselves over an area of interest. Each UAV uses only local neighbor information to advance the swarm toward a desired MANET topology. Simulation experiments in OPNET show that our algorithms can provide high percentage area coverage over a target, while requiring limited near neighbor communication. They are lightweight and power efficient, hence well-suited for military applications.
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AI Based Flight Control for Autonomous UAV Swarms
Semantic Scholar · Engineering · 2018
Abstract
Artificial intelligence (AI) based flight control algorithms can be successfully utilized to deploy a swarm of autonomous Unmanned Areal Vehicles (UAVs). In a swarm of autonomous UAVs operating as a mobile ad-hoc network (MANET), use of centralized control, pre-planned missions, synchronization of nodes and reliance on conditional procedures are not feasible We introduce near real-time AI based flight control algorithms for autonomous UAVs to position themselves over an area of interest. Each UAV uses only local neighbor information to advance the swarm toward a desired MANET topology. Simulation experiments in OPNET show that our algorithms can provide high percentage area coverage over a target, while requiring limited near neighbor communication. They are lightweight and power efficient, hence well-suited for military applications.