Mission-Based Energy Consumption Prediction of Multirotor UAV
Mission-Based Energy Consumption Prediction of Multirotor UAV
复制标题
DOI：
10.1109/access.2019.2903644
复制
发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Wang, Yu-Kai
中科院分区：
文献类型：
--
作者：
Prasetia, Alex S.;Wai, Rong-Jong;Wang, Yu-Kai
无人飞行器（UAV）是近年来众多研究热点之一。其用途的多样性使其结构和控制研究成为关注的焦点。但是，在不知道每次任务将消耗的能量的情况下，可用的飞行时间将是未知的，这种运载工具的使用将受到限制。提出了一种基于任务的无人机能耗预测黑匣子建模方法。该设置由ArduPilot与任务规划器固件安装到定制的六旋翼。该方法包括三个连续的步骤：数据收集、数据预处理和回归。为了收集所需的数据，定义了包含几种运动类型的飞行模式，其中收集了包含任务、GPS和电池的飞行数据日志。预处理包括运动分离，也包括水平运动的加减速。最后，使用Sklearn的Elastic Net regression进行回归。然后在两种飞行模式上对该模型进行了测试，以模拟无人机的监视应用，并且可以以98.773%的平均能量精度预测从起飞到返回发射命令结束的任务。
Unmanned aerial vehicle (UAV) is lately one of many popular research topics. High variety of its usage makes it attentive to be studied on its construction or the control. However, without knowing the energy that will be consumed in each mission, the available flight duration will be unknown and the usages of this vehicle will be limited. A mission-based black box modeling of UAV's energy consumption prediction was proposed in this paper. The setup consists of ArduPilot with Mission Planner Firmware installed to a custom built hexarotor. The method consists of three consecutive steps: data collection, data preprocessing, and regression. To collect the required data, flight patterns that contained several types of movements were defined, where the flight data log that contained missions, GPS, and battery, was collected. The preprocessing included the movement separation and also included the acceleration and the deceleration of horizontal movement. Finally, the regression was done using the Elastic Net Regression from Sklearn. The model was then tested on two flight patterns to simulate a surveillance application of a UAV and could predict with 98.773% mean of energy accuracy of the missions that started from the takeoff and ended with the return to launch command.