## 4 UAV Navigation and Control
### UAV Placement  
In most studies, UAVs are deployed to deliver wireless communication coverage for stationary or quasi-stationary UEs [120]. Examples are on-demand coverage when the terrestrial cellular network is not available or congested due to natural disasters or social events [121]. Further, a team of UAVs may be adopted to create an aerial cellular network [122], where RISs are deployed on uneven terrains to assist the UAV-BSs in eliminating coverage blind spots and extending coverage. In such situations, the 3D allocation and placement of the UAV(s) are important factors in enhancing the coverage and channel performance [75], e.g., there is a compromise between channel gain and coverage [49].  
The placement of the RIS-assisted UAV-BS has been considered in [80] to provide wireless channels for blocked ground users. The UAV-BS placement problem is solved using the Adam optimizer, a gradient-based optimization, which is a popular optimization algorithm for solving stochastic optimization problems in machine (deep) learning [80]. The RISeUAV takes advantage of both UAVs and RISs simultaneously to improve future WCNs by reflecting signals in the sky. However, high-quality aerial LoS links, imperfect CSI in highly dynamic environments, etc., make the RISeUAV-assisted channel links more vulnerable to both adversarial eavesdropping and malicious jamming. In [81], a secure communication strategy for RISeUAV was proposed to maximize secrecy rates while meeting QoS requirements against eavesdroppers and jammers. The method jointly optimized transmit beamforming, artificial noise, RISeUAV placement, and RIS phase shift to handle mixed-attack scenarios. To solve the non-convex optimization problem in dynamic environments, a post-decision state deep Q-network (DQN) with Fourier feature mapping (DQN-FFM) was introduced for robust anti-attack transmission  
With STAR-RISs, the coverage of the RISs is extended to 360 degrees by simultaneous transmission and reflection [123]. RISeUAV with STAR-RIS can increase transmission security by reducing interference and controlling unfavorable environments for signal eavesdropping. Therefore, the placement of RISeUAV with STAR-RIS has been studied [82]. An iterative method was used for UAV placement in static scenarios, with fractional programming, SCA for power control, semidefinite relaxation for transmission/reflection design, and a search-based approach for STAR-RIS positioning. For mobile scenarios, a double DQN (DDQN) algorithm was employed to learn an online UAV path design policy from a long-term perspective.  
Further, RISeUAV can be beneficial in a maritime communication system against jamming attacks [83]. The RISeUAV placement and RIS phase shift are determined using the DRL technique targeting energy efficiency, BS transmit power constraints, energy harvesting in simultaneous wireless information and power transfer (SWIPT), and QoS requirements for maritime UEs.  
To obviate concerns regarding the limited onboard energy and payload, the application of a tethered UAV as an airborne BS has been proposed [78], where the tether provides a stable power supply and backhauling [124]. The limitation imposed by the limited length of the tether, particularly of cell-edge UEs, can be compensated by mounting RIS on the surrounding buildings. The placement of the tethered UAV along with the RIS phase shift, is optimally determined in [78] to maximize the weighted sum data rate (WSDR) of ground users by using RSMA as communication technology.  
The coverage area may be large and one UAV cannot cover all UEs; therefore, a network of UAVs can be employed [84]. In these situations, the hovering position of UAVs is determined by taking the inter-UAV distance as a constraint.