The recent advancements in multi-agent systems and UAV technologies have significantly pushed the boundaries of autonomous coordination and control in various applications. A notable trend is the integration of deep reinforcement learning (DRL) to optimize task scheduling and trajectory planning in complex, high-dimensional environments. This approach allows for scalable solutions that can adapt to varying numbers of agents, enhancing operational efficiency and reducing human intervention. Additionally, the development of nature-inspired algorithms for collision-avoidance and formation control in 3D spaces has shown promise in urban and aerospace scenarios. The emphasis on real-time systems and middleware for seamless execution of high-level scheduling algorithms further bridges the gap between theoretical advancements and practical deployment. Notably, the use of data compression in mobile edge computing to optimize energy consumption and task offloading has demonstrated significant efficiency gains. Overall, the field is moving towards more autonomous, scalable, and energy-efficient solutions, with a strong focus on real-world applicability and robustness.