E2PP: An Energy-Efficient Path Planning Method for UAV-Assisted Data Collection
E2PP: An Energy-Efficient Path Planning Method for UAV-Assisted Data Collection
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E2PP：一种用于无人机辅助数据采集的节能路径规划方法
DOI：
10.1155/2020/8850505
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发表时间：
2020-12-02
影响因子：
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通讯作者：
Fang, Dingyi
中科院分区：
文献类型：
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作者：
Ji, Xiang;Meng, Xianjia;Fang, Dingyi
使用无人机从部署在现场的无线传感器网络收集数据，关键任务之一是规划收集路径，以最大限度地减少无人机的能耗。目前，大多数现有方法普遍以最短飞行距离为最优目标来规划最优路径。他们简单地认为最短路径意味着无人机的能量消耗最少，而忽略了改变方向（航向）也会消耗无人机在飞行过程中的能量这一事实。如果能够根据无人机的能耗进行更接近真实情况的路径规划，就可以真正降低无人机的能耗，提高其工作能效。因此，本文提出一种无人机辅助数据采集的路径规划方法，可以规划节能的飞行路径。首先，通过分析实验数据，分别对航向变化角度与无人机能耗之间的关系以及直线飞行距离与无人机能耗之间的关系进行建模。然后，建立了基于距离和航向变化角度的能耗估算模型（ECEMBDA）。通过使用该模型，我们可以估计或预测无人机从一个点（或节点）飞往另一点（包括起点）的能耗。最后根据上述估计的能耗，利用贪心算法规划无人机辅助数据采集的路径。通过仿真和实验，我们将所提出的方法与基于纯距离指数和贪心算法的传统方法进行了比较。结果表明，该方法可以获得能耗更低、路径轨迹更平滑的数据采集路径，更适合实际飞行。
Using an unmanned aerial vehicle (UAV) to collect data from wireless sensor networks deployed in the field, one of the key tasks is to plan the path for the collection so as to minimize the energy consumption of the UAV. At present, most of the existing methods generally take the shortest flight distance as the optimal objective to plan the optimal path. They simply believe that the shortest path means the least energy consumption of the UAV and ignore the fact that changing direction (heading) can also consume the UAV’s energy in its flight. If the path can be planned based on the UAV’s energy consumption closer to the real situation, the energy consumption of the UAV can be really reduced and its working energy efficiency can be improved. Therefore, this paper proposes a path planning method for UAV-assisted data collection, which can plan an energy-efficient flight path. Firstly, by analyzing the experiment data, we, respectively, model the relationship between the angle of heading change and the energy consumption of the UAV and the relationship between the distance of straight flight and the energy consumption of the UAV. Then, an energy consumption estimation model based on distance and the angle of heading change (ECEMBDA) is put up. By using this model, we can estimate or predict the energy consumption of a UAV to fly from one point (or node) to another (including the start point). Finally, the greedy algorithm is used to plan the path for UAV-assisted data collection according to the above estimated energy consumption. Through simulation and experiments, we compare our proposed method with the conventional method based on pure distance index and greedy algorithm. The results show that this method can obtain data collection path with lower energy consumption and smoother path trajectory, which is more suitable for actual flight.