The field of aerial robotics and edge computing is rapidly evolving, with a focus on developing innovative solutions for efficient communication, data collection, and energy transfer. Researchers are exploring the use of unmanned aerial vehicles (UAVs) to enhance edge computing capabilities, particularly in vehicular networks. The integration of UAVs with edge computing is enabling the development of more efficient and adaptive offloading strategies, which can minimize system delay and energy consumption. Additionally, advances in reinforcement learning and optimization techniques are being applied to improve the performance of aerial robots in tasks such as perching and trajectory planning. Noteworthy papers include:

• A paper proposing a hierarchical task offloading scheme for UAV-assisted vehicular edge computing, which demonstrates strong robustness and applicability in dynamic environments.
• A paper presenting a novel trajectory framework for tethered tensile perching, which achieves precise control over position and velocity.
• A paper employing network calculus tools to derive probabilistic upper bounds on communication delay in eVTOL systems, addressing a critical challenge in Advanced Air Mobility.
• A paper proposing a UAV-assisted data collection and wireless power transfer framework for batteryless sensor networks, which improves exploration efficiency and learning stability in complex scenarios.