# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 4 Application of UAV-Aided Non-Terrestrial Networks in UAIS
### Unmanned Ground Vehicle in UAV-Aided NTN Communication
#### 4.1.2 NTN Communication in Unmanned Ground Vehicle Control  
In the future, 6G network ultra-reliable and low-latency communication (URLLC) will be essential for UGVs, which are indispensable for real-time applications such as smart agriculture [113; 114; 115], intelligent industry, and various other applications. To accurately control the UGV, tactile feedback and interactive virtual reality (VR) can stimulate the human brain, aiding users in adjusting operation time, pressure, and gestures. These factors are crucial for remote operations. The tactile feedback and interactivity of VR applications require low-latency and broadband networks. URLLC is necessary to deliver extremely low latency for real-time feedback for operators, based on short-length packets. To address this challenge, ref. [113] proposed an approach that utilized UAVs as forward relays to transmit short URLLC control packets between a control station and multiple ground mobile vehicles in smart agriculture scenarios. Researchers [116] proposed a radar-aided millimeter-wave communication method for UAV-UGV cooperation in a disaster scenario where ground network infrastructure is available. In addition, the UAV-UGV cooperation networks were further investigated in terms of bandwidth allocation [117], data collection [118], navigation [118; 119; 120], and other related aspects. When UGVs are utilized for surveillance or disaster relief missions in less populated areas lacking terrestrial infrastructure, UAV-aided networks are valuable for controlling UGVs and directing them to the target area. For high-speed maneuvering platforms, achieving high-speed communication presents a challenging problem. Researchers in [116] proposed a mm-wave radar-aided communication scheme for UAV-UGV cooperation. The worst-case QoS maintain method for UGVs was developed using QoS-awareness power distribution. However, in reference [121], a software-defined radio method was utilized to extend the communication range between UAV and UGV using OFDM. The study enhanced UAV UGV communication by introducing a new waveform based on OFDM to increase the likelihood of line-of-sight links. Researchers of [122] considered the target perception and tracking problem utilizing a UAV-UGV group. The study involved a heterogeneous communication scenario in which consensus control was achieved in the case of changing topology. Furthermore, the authors of [123] introduced a novel communication protocol for the collaboration of air-ground platforms. The cooperative communication scheme allowed inter-communication between UAVs and UGVs through a client-server architecture. In [124], a joint path optimization scheme was proposed to enable the optimal recharging location of the UAVs for wireless communication. Ref. [125] considered a path optimal problem of one UAV and multiple UGVs wherein the UAVs provide communication coverage and share information with the UGVs. A sequence strategy was utilized to determine the access sequence of the UGVs. UGVs are also promising platforms for extending network coverage to expand service availability and provide network connectivity for other ground users. In reference [126], a graph-based optimization method for virtual resource allocation of slices was proposed in ground vehicle-assisted networks. A system modeling procedure inspired by graph theory was used to model networks and the energy cost of vehicle-assisted B5G networks. The rapid advancements in autonomous technologies have also promoted the development