The field of autonomous systems and wireless networks is rapidly advancing, with a focus on developing intelligent and adaptive mechanisms to ensure efficient routing, resource utilization, and dynamic obstacle avoidance. Recent research has highlighted the potential of combining machine learning with clustering techniques to enhance scalability, stability, and performance in next-generation aerial networks. Additionally, there is a growing interest in using data-driven approaches, such as Koopman operator theory and ensemble Hankel dynamic mode decomposition, to model and predict complex dynamics in autonomous systems. These approaches have shown promising results in improving safety margins, robustness, and uncertainty quantification. Notable papers in this area include:

• A study on cluster-based routing using machine learning for UAV networks, which achieved significant improvements in delay, jitter, and throughput.
• A paper on real-time learning of predictive dynamic obstacle models for robotic motion planning, which demonstrated stable variance-aware denoising and short-horizon prediction.
• A research on Koopman-based prediction of connectivity for flying ad hoc networks, which accurately predicted connectivity and isolation events.
• A study on risk-aware safe control with cooperative sensing for dynamic obstacle avoidance, which improved safety margins and robustness.
• A paper on data-driven uncertainty-aware seakeeping prediction using ensemble Hankel dynamic mode decomposition, which provided reliable and computationally efficient seakeeping prediction for design and operational support.