Wireless networks consisting of low size, weight, and power, fixed-wing unmanned aerial vehicles (UAVs) are used in many applications, such as search, monitoring, and information gathering of inaccessible areas, in which UAVs sense within an area and forward the information, in a multihop manner, to an aerial base station (BS). Robustly performing these tasks requires the UAV network to be decentralized, autonomous, and scalable. An important tradeoff is between area coverage and connectivity: fast area coverage is needed to quickly identify objects of interest, while connectivity must be maintained for coordination and to transmit sensed information to the BS in real time. These factors must be balanced by the mobility model, which for each UAV has access only to locally available information. While Adam (2019, 2020) attempts to balance these factors using flocking behavior, this only encourages the UAVs to spread, rather than using knowledge of what areas have already been covered. In this article, we develop a neighborhood- and BS-connectivity-aware distributed pheromone mobility model to autonomously coordinate the UAV movements in a decentralized network. By using a pheromone map, we directly incorporate recent coverage information for the area. We then extend our approach to a deep $Q$-learning policy variant to further tune and improve the balance between coverage and connectivity. These mobility models are fully distributed and rely only on information from neighboring UAVs. Our simulations demonstrate that both the models achieve efficient area coverage and improved connectivity (both locally and to the BS), providing significant improvements over existing approaches.
A Deep-$Q$-Learning-Based Base-Station-Connectivity-Aware Decentralized Pheromone Mobility Model for Autonomous UAV Networks
IEEE Transactions on Aerospace and Electronic Systems ; 60 , 6 ; 8682-8699
2024-12-01
7499811 byte
Article (Journal)
Electronic Resource
English
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