In response to the path planning problem of using Unmanned Aerial Vehicle (UAV) for blood transportation, with the objective of minimizing the total distance travelled by the UAV, a multi-constraint drone blood transportation path planning model is established. Taking into account the limited number of drone take-off and landing platforms and the safety time intervals for continuous drone take-off and landing, a drone take-off scheduling strategy is designed to reduce the total time spent by UAV completing transportation tasks. Additionally, an Imperial Competition Algorithm based on Imperialist Competitive Algorithm is proposed to solve this problem. This algorithm introduces a sine disturbance strategy and adds Imperial Reform stages to improve the search accuracy of the algorithm. It utilizes acceptance criteria related to solution quality to maintain the diversity of the population. Validation is conducted using benchmark examples and instances of drone blood transportation. The results indicate that the proposed algorithm can provide transportation solutions for drone blood transportation tasks that meet various constraints without any conflicts in drone take-off and landing. The drone take-off scheduling strategy effectively reduces the total time spent by UAV in completing tasks.

The use of Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAVs), also known as drones, is changing the way future communication and networking systems are designed. UAVs can collect data, support wireless networks, and help deliver services from the sky, which makes them an important part of modern technology. To understand these developments, we reviewed almost 250 research papers published between 2015 and 2024. Our review focuses on UAV network design, communication methods, energy management, AI-based optimization, and future challenges. Unlike previous surveys that mainly summarize individual technical domains, this work introduces a new AI-driven UAV classification framework that connects these aspects under one structure. The framework organizes UAV systems across five dimensions–mission adaptability, autonomy level, communication intelligence, scalability, and deployment context–providing a unified way to compare current and future UAV technologies. This analytical structure highlights how artificial intelligence enables UAVs to move from static, pre-defined operations toward dynamic, real-time decision-making and mission-specific adaptation. We found that deep learning and reinforcement learning are the most common AI methods used to improve routing, flight planning, resource use, and network performance. These techniques help UAV networks adapt to changing conditions and reduce communication delays. However, we also found several open challenges, such as improving real-time energy efficiency, increasing security and privacy, managing large drone groups (swarms), and dealing with regulatory and policy issues. By combining this new framework with an extensive literature review, the paper offers a holistic view that not only summarizes past progress but also maps existing gaps and trends for future research. This paper provides a clear summary of current research, explains key trends, and points out gaps such as the need for lightweight AI models and better swarm coordination. The insights from this review can help researchers and engineers build smarter, safer, and more efficient UAV networks in the future.