论文标题
无人机能源消耗模型的比较研究
A Comparative Study on Energy Consumption Models for Drones
论文作者
论文摘要
创建适当的能源消耗预测模型正在成为文献中与无人机相关研究的重要主题。但是，目前尚未达成关于能源消耗模型的一般共识。结果，有许多变化试图创建复杂性范围的模型，重点关注不同方面。在本文中，我们基于从其物理行为中得出的五个最受欢迎的无人机消耗模型，并指出了与在不同测试条件下从飞行中的交付无人机中收集的现实能量数据集匹配的困难。此外，我们使用基于长期的短期记忆（LSTM）深度学习体系结构提出了一种新型的数据驱动能量模型，并根据数据集比较准确性。我们的实验结果表明，基于LSTM的方法可以轻松地超过所研究数据集的其他数学模型。最后，为了解释模型进行了灵敏度分析。
Creating an appropriate energy consumption prediction model is becoming an important topic for drone-related research in the literature. However, a general consensus on the energy consumption model is yet to be reached at present. As a result, there are many variations that attempt to create models that range in complexity with a focus on different aspects. In this paper, we benchmark the five most popular energy consumption models for drones derived from their physical behaviours and point to the difficulties in matching with a realistic energy dataset collected from a delivery drone in flight under different testing conditions. Moreover, we propose a novel data-driven energy model using the Long Short-Term Memory (LSTM) based deep learning architecture and the accuracy is compared based on the dataset. Our experimental results have shown that the LSTM based approach can easily outperform other mathematical models for the dataset under study. Finally, sensitivity analysis has been carried out in order to interpret the model.
