## 8 Future Research Directions  
Despite significant advancements in UAV and RIS-assisted wireless communication systems, several areas require further exploration to fully realize the potential of these technologies in future B5G/6G networks. Below, we outline key open research topics:  
1. Obstacle-free trajectory optimization in smart cities: Navigating complex, obstacle-rich environments in smart cities demands advanced optimization techniques. Future research should focus on developing robust algorithms that ensure obstacle-free trajectories while maintaining energy efficiency and communication reliability. Addressing this challenge is vital for safe and seamless UAV operations in urban settings.
2. Channel modeling for RISeUAV systems: The impact of UAV fluctuations due to disturbances such as wind or misalignment must be accounted for in channel modeling for RIS-assisted UAVs. Developing accurate models that consider these variables and incorporate UAV motion primitives adaptable to UAV dynamics will improve the resilience and effectiveness of communication links.
3. AI-based path planning with visual inputs: While AI and machine learning techniques have been employed for UAV path planning, more attention is needed on learning-based end-to-end methods [203], particularly those leveraging visual inputs. These approaches can enable UAVs to make complex navigation decisions in real time, which is crucial for applications in dense urban environments where dynamic obstacles and complex terrains are common.
4. Modern AI and transformer neural networks for trajectory optimization and RIS control: The use of modern AI architectures, such as transformer neural networks and visual transformers (ViT), present promising avenues for optimizing UAV trajectories and RIS phase shift control. Transformers can process large datasets and capture long-term dependencies effectively, making them suitable for real-time decision-making in UAV navigation and RIS configuration.
5. Integrated localization and communication: The integration of localization and communication functions is critical for enhancing the reliability and efficiency of UAV-based systems. Future works should explore methods to synergize these functions, ensuring accurate positioning while maintaining robust communication links. Open research areas include joint algorithm development, hardware design, and real-time implementation strategies.
6. Computationally feasible solutions for autonomous navigation: Developing algorithms that balance computational efficiency with performance accuracy is essential  
Figure 8: The geometry of the SLAPS for RISeUAV.  
for enabling real-time autonomous UAV navigation. Current methods often struggle to incorporate kinodynamic and motion constraints while maintaining obstacle-free trajectories, especially in complex urban environments. Future research should focus on creating lightweight, scalable algorithms capable of handling high-dimensional data and constraints without excessive computational demands. Strategies such as model predictive control with simplified dynamics [204], hybrid offline-online approaches, and leveraging advanced AI techniques could provide a path forward to achieving real-time, reliable autonomous navigation.
* Satellite-aided communication: Leveraging satellite communications for enhancing connectivity in RIS-UAV-assisted IoT and vehicular networks should be explored. This includes multi-layered communication frameworks that combine terrestrial, aerial, and satellite links for improved coverage and reliability.
* Security and privacy concerns: Ensuring the security and privacy of data transmission in UAV-assisted, RIS-enabled IoT and vehicular networks is another area that needs further investigation. Techniques for protecting against eavesdropping, jamming, and spoofing, as well as secure protocols for data exchange, remain essential topics for future research.