When performing a search and rescue mission, unmanned aerial vehicles (UAVs) should continuously search targets above the mission area. In order to transfer the search and rescue information quickly and efficiently, two types of UAVs, the ferrying UAVs and the searching UAVs, are used to complete the mission. Obviously, this application scenario requires an efficient path planning method for ferrying UAVs. The existing path planning methods for ferrying UAVs usually focus on shortening the path length and ignore the different initial energy of ferrying UAVs. However, the following problem does exist: if the ferrying UAV with less initial energy is assigned a longer path, meaning that the ferrying UAV with less initial energy will ferry messages for more searching UAVs. When the lower-initial-energy ferrying UAV is running out of energy, more searching UAVs will no longer deliver messages successfully. Therefore, the mismatch between the planned path length and the initial energy will eventually result in a lower global message delivery ratio. To solve this problem, we propose a new concept energy-factor for a ferrying UAV and use the variance of all ferrying UAVs’ energy-factor to measure the balance between the planned path length and the initial energy. Further, we model the energy-balanced path-planning problem for multiple ferrying UAVs, which actually is a multiobject optimization problem of minimizing the planned path length and minimizing the variance of all ferrying UAVs’ energy-factor. Based on the genetic algorithm, we design and implement an energy-balanced path planning algorithm (EMTSPA) for multiple ferrying UAVs to solve this multiobject optimization problem. Experimental results show that EMTSPA effectively increases the global message delivery ratio and decreases the global message delay.

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基于遗传算法的多轮无人飞行器能量平衡路径规划算法

执行搜索和救援任务时，无人飞行器（UAV）应在任务区域上方连续搜索目标。为了快速有效地传递搜索和救援信息，使用了两种类型的无人机，即渡轮无人机和搜索无人机，以完成任务。显然，这种应用场景需要一种有效的路径规划方法来运送无人机。现有的用于运送无人机的路径规划方法通常集中在缩短路径长度上，而忽略了运送无人机的不同初始能量。但是，确实存在以下问题：如果为初始能量较小的渡轮无人机分配了更长的路径，则意味着初始能量较小的渡轮无人机将为更多的搜索无人机运送消息。当较低初始能量的渡轮无人机能量耗尽时，更多的搜索无人机将不再成功传递消息。因此，计划的路径长度和初始能量之间的不匹配最终将导致较低的全局消息传递比率。为了解决这个问题，我们提出了一种新概念的渡轮无人机能量因数，并使用所有渡轮无人机能量因数的方差来衡量计划的路径长度和初始能量之间的平衡。此外，我们对多轮无人机的能量平衡路径规划问题进行建模，这实际上是一个最小化计划路径长度并最小化所有轮渡无人机能量因数的多目标优化问题。基于遗传算法，我们设计并实现了多轮无人机的能量平衡路径规划算法（EMTSPA），以解决该多目标优化问题。