Hu Zhonghua, Liu Shihao
No.38 Institution of CETC, Hefei, China.
School of Mechanical and Electrical Engineering, Hainan University, Haikou, China.
Sci Prog. 2023 Jul-Sep;106(3):368504231187498. doi: 10.1177/00368504231187498.
The solar unmanned aerial vehicle (UAV) route planning needs to comprehensively consider the conversion efficiency of solar cells under the influence of solar ground reflection radiation and sky scattering radiation. On the one hand, it is necessary to consider the cost of radar threat, mileage energy consumption, mountain impact and other costs. On the other hand, it is also necessary to consider the influence of high threat, mountain shadow occlusion cost, and cloud shading cost on solar photovoltaic conversion efficiency. The above problem was solved through using the ant colony intelligent optimization algorithm. By constructing ant colony paths rationally, models of mountain impact cost, high threat, mountain shadow shelter cost, and cloud shading cost were established. The constraints such as the maximum action distance, solar irradiation angle and effective action distance of various costs were introduced into the cost model and exploration factor calculation, and the comprehensive optimization problem of solar UAV route was solved. Finally, the simulation results show that the algorithm path structure is reasonable; the target node can be found independently; the convergence speed can meet the requirements of route planning; the generated route cost is small; the algorithm is reasonable and effective.
太阳能无人机（UAV）的航线规划需要综合考虑在太阳地面反射辐射和天空散射辐射影响下太阳能电池的转换效率。一方面，需要考虑雷达威胁成本、航程能耗、山地影响等成本。另一方面，还需要考虑高威胁、山体阴影遮挡成本以及云层遮蔽成本对太阳能光伏转换效率的影响。通过使用蚁群智能优化算法解决了上述问题。通过合理构建蚁群路径，建立了山地影响成本、高威胁、山体阴影遮蔽成本和云层遮蔽成本模型。将各种成本的最大行动距离、太阳照射角度和有效行动距离等约束条件引入成本模型和探索因子计算中，解决了太阳能无人机航线的综合优化问题。最后，仿真结果表明该算法路径结构合理；能够自主找到目标节点；收敛速度能够满足航线规划要求；生成的航线成本小；算法合理有效。
