| Title |
Research on Improving the Performance of English Machine Translation Through Optimal Extraction of Language Vector Features |
| DOI |
https://doi.org/10.5573/IEIESPC.2025.14.3.400 |
| Keywords |
Semantic feature; Machine translation; English; Text vectorization |
| Abstract |
Drones have shown enormous potential in urban logistics due to their efficiency and flexibility. However, traditional path planning methods such as particle swarm optimization and A* algorithm often find it difficult to meet both efficiency and safety when used alone. Therefore, this study proposes a new unmanned aerial vehicle logistics distribution path planning method. By adjusting parameters and optimizing search strategies, the particle swarm optimization algorithm utilizes the efficient pathfinding ability of A* algorithm to ensure its security. The results show that the obstacle avoidance success rate of the model is 94.85%, which is the best performance compared to other comparative algorithms and provides the shortest and smoothest path selection. This method demonstrates good path planning efficiency and stability, improving logistics and distribution capabilities in urban environments.
This provides valuable reference for intelligent path planning and intelligent transportation systems. |