The field of unmanned aerial vehicles (UAVs) is moving towards more advanced navigation and optimization techniques to improve their performance in complex environments. Researchers are focusing on developing autonomous systems that can adapt to dynamic scenarios, reduce energy consumption, and improve rescue efficiency. The use of metaheuristic algorithms, such as Henry gas optimization, is becoming increasingly popular for optimizing UAV trajectories and reducing transportation costs. Additionally, real-time obstacle avoidance algorithms and adaptive navigation strategies are being developed to enable safe and efficient UAV operation in unfamiliar and hazardous environments. Noteworthy papers include: Autonomous Trajectory Optimization for UAVs in Disaster Zone Using Henry Gas Optimization Scheme, which proposes a cluster optimization scheme using the Henry gas optimization metaheuristic algorithm to identify the shortest path with minimal transportation cost and algorithm complexity. EANS: Reducing Energy Consumption for UAV with an Environmental Adaptive Navigation Strategy, which proposes a method to dynamically adjust the navigation strategy of UAVs to reduce energy consumption in response to environmental changes.