Zhang Weijian, Li Jian, Yu Weilin, Ding Peng, Wang Jiawei, Zhang Xuen
College of Information Technology, Jilin Agricultural University, Changchun, China.
Bioinformatics Research Center of Jilin Province, Changchun, China.
Front Plant Sci. 2024 Oct 17;15:1391628. doi: 10.3389/fpls.2024.1391628. eCollection 2024.
Path planning is one of the key elements for achieving rapid and stable flight when unmanned aerial vehicles (UAVs) are conducting monitoring and inspection tasks at ultra-low altitudes or in orchard environments. It involves finding the optimal and safe route between a given starting point and a target point. Achieving rapid and stable flight in complex environments is paramount. In environments characterized by high-density obstacles, the stability of UAVs remains a focal point in the research of path planning algorithms. This study, utilizing a feature attention mechanism, systematically identifies distinctive points on the obstacles, leading to the development of the RFA-Star (R5DOS Feature Attention A-star) path planning algorithm. In MATLAB, random maps were generated to assess the performance of the RFA-Star algorithm. The analysis focused on evaluating the effectiveness of the RFA-Star algorithm under varying obstacle density conditions and different map sizes. Additionally, comparative analyses juxtaposed the performance of the RFA-Star algorithm against three other algorithms. Experimental results indicate that the RFA-Star algorithm demonstrates the shortest computation time, approximately 84%-94% faster than the RJA-Star algorithm and 51%-96% faster than the Improved A-Star. The flight distance is comparable to the RJA-Star algorithm, with slightly more searched nodes. Considering these factors collectively, the RFA-Star algorithm exhibits a relatively superior balance between computational efficiency and path quality. It consistently demonstrates efficient and stable performance across diverse complex environments. However, for comprehensive performance enhancement, further optimization is necessary.
路径规划是无人机在超低空或果园环境中执行监测和检查任务时实现快速稳定飞行的关键要素之一。它涉及在给定起点和目标点之间找到最优且安全的路线。在复杂环境中实现快速稳定飞行至关重要。在以高密度障碍物为特征的环境中，无人机的稳定性仍然是路径规划算法研究的重点。本研究利用特征注意力机制，系统地识别障碍物上的独特点，从而开发出了RFA-Star（R5DOS特征注意力A*）路径规划算法。在MATLAB中，生成随机地图以评估RFA-Star算法的性能。分析重点在于评估RFA-Star算法在不同障碍物密度条件和不同地图大小下的有效性。此外，通过对比分析将RFA-Star算法的性能与其他三种算法进行了并列比较。实验结果表明，RFA-Star算法的计算时间最短，比RJA-Star算法快约84%-94%，比改进的A*算法快51%-96%。飞行距离与RJA-Star算法相当，搜索节点略多。综合考虑这些因素，RFA-Star算法在计算效率和路径质量之间表现出相对优越的平衡。它在各种复杂环境中始终展现出高效稳定的性能。然而，为了全面提升性能，还需要进一步优化。
