基于数据手套的无人机端侧控制系统设计

为克服复杂环境下，遥控器、地面站等传统控制设备无法灵活便捷控制无人机的问题，提出一种基于数据手套的无人机端侧控制系统，该系统能够实现通过手势对无人机的控制。首先采用集成柔性传感器和惯性传感器，基于STM32的无线数据手套，用于收集训练和测试数据。根据从数据手套中获得的数据，采用部署到STM32嵌入式处理器的BP神经网络进行端侧手势识别，最后将手势转换成标定的无人机控制指令，并发送到无人机端，实现对无人机的控制。对8种手势进行共400次识别验证，手势识别率为97%。通过Airsim仿真平台进行无人机仿真实验，8种手势对应的无人机基本控制指令识别 准确率为100%，表明该系统手势识别效果理想。最终在真实场景下进行试飞，多名参与者可在1分钟内完成提前规定的总路线长度为35m的复杂飞行，实验表明无人机可对手势做出迅速反应，且该系统提供的手势控制方法简单便捷，可以在端侧实现对无人机的实时稳定地控制。

Design of UAV end-side control system based on data glove

In order to overcome the problem that traditional control equipment such as remote controller and ground station can not control UAV flexibly and conveniently in complex environment, a UAV end-side control system based on data glove is proposed, which can control UAV through gesture. Firstly, a wireless data glove based on STM32, which integrates flexible sensors and inertial sensors, is used to collect training and test data. According to the data obtained from the data glove, BP neural network deployed to STM32 embedded processor is used for end-side gesture recognition. At last, the gesture is converted into matched UAV control command and sent to the UAV to realize the control. A total of 400 recognition verifications were carried out for 8 kinds of gestures, and the gesture recognition rate was 97%. The UAV simulation experiment is carried out through the Airsim simulation platform. The recognition accuracy of the basic control commands of UAV corresponding to eight gestures is 100%, which shows that the gesture recognition effect of the system is ideal. Finally, the test flight is carried out in the real scene, and multiple participants can successfully complete the complex flight with a total route length of 35m specified in advance within 1 minute. The experiment shows that the UAV can respond quickly to the gesture, and the gesture control method provided by the system is simple and convenient, which can realize the real-time and stable control of the UAV at the end side.