Drone Motion Primitive Dataset

Drone technology is one of the largest tackled fields in today’s world, as it can range from pure enjoyment of drone racing to medical use and fighting crime. Several teams are interested in developing improved human machine interfaces for operating drones. This dataset is a collection of different motion primitives commanded using a PS3 joystick to control an Ardrone on Gazebo. This has been conducted using ROS Melodic on Ubuntu 18.04. The PS3 six-axis joystick was used to collect this data, and it was connected to the Gazebo drone by using the ArDrone Helpers libraries.This dataset contains 4 different motion primitives which are: Line, Helix, Sine, and Arc. The testing was conducted on each primitive alone and using a stop indicator, which is specified for each primitive, at the end of each primitive. The stop indicators are as follows:· X for Sine· Circle for Helix· Square for Line· Triangle for ArcThen a collection of every two primitives consecutively was also conducted (i.e. line to helix, arc to line, helix to sine …) using the same stop indictors at the end of every corresponding primitive. Each test has around 300 to 500 samples. It is also important to note that our data is sample and hold, meaning that if the joystick’s analog stick is in a certain position for a few seconds, then it will not send any new samples for the velocities.The data was extracted into a .YAML file, as continuous commands from the joystick which were used to move the drone in Gazebo. They are then parsed (using python 2.7) to extract the velocities of the drone along the x y z axes as well as the pressed buttons (x or circle or square or triangle) into a .CSV file.This parsed data is further processed (using python3) into another .CSV file containing all the velocities as arrays, a histogram to get the average number of samples for every sequence, and a .RTF file which contains some analysis for each primitive or collection of primitives (number of samples taken, min and max sequence size, mean, median, and deviation).N.B: for the sine primitive, the data has been extracted into two categories: one where the sine was along the x-y axes and another where it was along the x-z or y-z axes. The github link for the two codes used is: https://github.com/justinelhalabi/PS3-Joystick-Command-Parsing-into-CSV无人机技术是当今世界重点发展的领域之一，其应用场景覆盖从无人机竞速娱乐，到医疗应用、犯罪防控等诸多方向。目前已有多个团队致力于研发更优化的人机交互界面（human machine interfaces），以实现无人机的操控。本数据集收录了通过PS3六轴摇杆（PS3 six-axis joystick）操控Gazebo仿真平台中Ardrone无人机所生成的各类运动基元（motion primitives）数据。实验基于Ubuntu 18.04系统下的ROS Melodic环境开展，通过ArDrone Helpers库实现PS3六轴摇杆与Gazebo仿真无人机的连接，以此完成数据采集。 本数据集共包含4类独立运动基元，分别为直线（Line）、螺旋（Helix）、正弦（Sine）以及圆弧（Arc）。针对每一类运动基元，均开展了单独测试，并在每段基元运动结束时设置对应停止标识（stop indicator）：正弦运动对应按键X，螺旋运动对应圆形按键，直线运动对应方形按键，圆弧运动对应三角形按键。 此外，还开展了每两类运动基元连续衔接的组合测试（例如直线到螺旋、圆弧到直线、螺旋到正弦等），同样在每段对应基元运动结束时使用上述停止标识。单次测试的样本量约为300至500组。需要特别说明的是，本数据集采用采样保持（sample and hold）机制：若摇杆的模拟摇杆在数秒内保持同一位置，则不会发送新的速度采样数据。 原始数据以连续摇杆指令的形式提取为.YAML文件，用于在Gazebo中驱动无人机运动。随后通过Python 2.7对该文件进行解析，提取无人机沿x、y、z三轴的速度数据以及按键输入（X、圆形、方形或三角形），并将其保存为.CSV文件。 解析后的数据集将通过Python 3进行二次处理，生成三类输出文件：其一为包含所有速度数组的.CSV文件；其二为用于统计每段序列平均采样数的直方图数据；其三为.RTF文件，其中包含各类基元或基元组合的相关分析数据，包括采样数量、序列长度的最小值与最大值、均值、中位数及标准差。 注：针对正弦运动基元，数据被划分为两类：一类为沿x-y轴的正弦运动，另一类为沿x-z或y-z轴的正弦运动。本研究使用的两段代码的GitHub链接为：https://github.com/justinelhalabi/PS3-Joystick-Command-Parsing-into-CSV