The recent advancements in the field of unmanned aerial vehicle (UAV) swarm technology have shown significant progress in enhancing both autonomy and efficiency. Researchers are increasingly focusing on developing decentralized control methods that reduce reliance on explicit communication, thereby improving scalability and robustness in complex environments. Notably, bio-inspired approaches are being leveraged to enhance relative localization and swarm coordination, offering more scalable solutions for large-scale UAV operations. Additionally, adaptive grid-based decomposition algorithms are being employed to optimize coverage path planning, particularly in search and rescue missions, where time efficiency is critical. The integration of minimalistic sensory requirements and self-organizing behaviors in UAV swarms is also gaining traction, enabling operations in GNSS-denied environments. Furthermore, evolutionary computation techniques, such as genetic algorithms, are being utilized for path planning in obstacle-laden environments, ensuring complete coverage while minimizing resource consumption. These developments collectively push the boundaries of what is possible with UAV swarms, making them more versatile and reliable for a wide range of applications.

Noteworthy papers include one that presents a novel control method for UAVs in obstacle-laden environments using control barrier functions, and another that proposes an adaptive grid-based decomposition algorithm for efficient coverage path planning in maritime search and rescue operations.