# Formation Control Algorithm of Multi-UAV-Based Network Infrastructure
## 2 Related Work  
Multi-agent coordination problem has been studied in decades. As the application of the multi-robot system is varied and materialized, recent research focuses on the multi-agent control strategies to enhance the performance on the specific missions. Also, growing numbers of the mobile robots and the devices require more robust and reliable communication schemes on the lossy networks. This section briefly addresses the previous research on mobile networks and the formation control solutions that can be adapted to the multi-UAV management systems, and investigates the research in multi-UAV-based network service provisioning in detail to clarify the contribution of this paper. [PERSON] et al. [11] studied on the robustness of the mobile ad hoc network where the nodes change their connectivity frequently and unpredictably, and designed a framework for protocol classes to cope with the connection disturbances. In [12], the authors proposed the data collection strategies for a single mobile sink in wireless sensor networks. The authors demonstrated the strategies in several mobility model, and proved the performance of their work. [PERSON] et al. [13] proposed a fast recoverable routing scheme for UAVs using geographic information. The research on the mobile nodes and the sinks is valuable, since most of the nodes in the multi-UAV-based network infrastructure are moving on 2D or 3D spaces. In this paper we focus on the formation control of the multiple UAVs, so we leave the deep consideration on the network protocol as future work. [PERSON] et al. [14] addressed a decentralized coordination problem of multiple aircrafts and proves the optimality of their work by Nash equilibrium. Our algorithm also locates the multiple UAVs in decentralized way, but the specific objective that the access network traffic management of our system reforms the formation to environment-aware one by adding the other equations. In [15], the authors showed an approach to the multi-vehicle formation system in graph rigidity manner and proposes the generic split/rejoin schemes for the stability of the system. This work is similar to a part of our algorithm constructing and maintaining the ad hoc network of multiple UAVs, except for the difference that our algorithm intends to enlarge the network coverage area while considering the traffic concentration. [PERSON] [16] deeply worked on the aerodynamics of the multi-UAV environments and proposes a testbed for aerial control algorithm. While the testbed verifies the actual behavior of the multi-UAV when the formation control algorithm is applied, we evaluate the network performance of the multi-UAV network infrastructure with simulation study when our formation control algorithm is applied. [PERSON] [17] arranged distributed multi-agent coordination systems while categorizing the sub-technologies into optimization, consensus, control, and estimation. Our algorithm consists of (i) the formation control in distributed manner, (ii) the information sharing between the nearby UAVs, (iii) optimality analysis by Nash equilibrium, (iv) estimation by performance modeling. [PERSON] et al. [18] proposed a 3D positioning algorithm of the UAVs, considering the environment where the wireless charging stations are deployed. The mathematical models representing the wireless charging is notable, since extending the lifetime of the UAV is one of the crucial challenges in the UAV. However, this paper focuses on the performance improvement of the system using the static, position-optimized wireless charger. Our algorithm targets to the wide-spread mobile users, which move unpredictably. [PERSON] et al. [19] worked on the path loss model of the UAV base station in the urban environments. By adopting International Telecommunication Union (ITU) channel model, the research reduces the path loss error from the former models.