School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, China.
Sensors (Basel). 2020 Oct 14;20(20):5805. doi: 10.3390/s20205805.
A novel coaxial ducted fan aerial robot with a manipulator is proposed which can achieve some hover operation tasks in a corner environment, such as switching on and off a wall-attached button on the corner. In order to study the aerodynamic interference between the prototype and the environment when the aerial robot is hovering in the corner environment, a method for the comprehensive modeling of the prototype and corner environment based on the artificial neural network is presented. By using the CFD simulation software, the flow field of the prototype at different positions with the corner effect is analyzed. After determining the input, output and structure of the neural network model, the Adam and gradient descent algorithms are selected as the neural network training algorithms, respectively. In addition, to optimize the initial weights and biases of the neural network model, the genetic algorithm is precisely used. The three-dimensional prediction surfaces generated by the three methods of the neural network, kriging surface and the polynomial fitting are compared. The results show that the neural network has high prediction accuracy, and can be applied to the comprehensive modeling of the prototype and the corner environment.
提出了一种具有机械手的新型同轴管道风扇式空中机器人，它可以在角落环境中实现一些悬停操作任务，例如打开或关闭角落处的壁挂按钮。为了研究空中机器人在角落环境中悬停时原型机与环境之间的空气动力干扰，提出了一种基于人工神经网络的原型机和角落环境综合建模方法。利用 CFD 模拟软件，分析了原型机在具有角效应的不同位置的流场。在确定神经网络模型的输入、输出和结构后，分别选择 Adam 和梯度下降算法作为神经网络训练算法。此外，为了优化神经网络模型的初始权重和偏置，精确使用遗传算法。比较了神经网络、克里金曲面和多项式拟合三种方法生成的三维预测曲面。结果表明，神经网络具有较高的预测精度，可应用于原型机和角落环境的综合建模。
