Li Mingjun, Cai Zhihao, Zhao Jiang, Wang Yibo, Wang Yingxun, Lu Kelin
School of Automation Science and Electronic Engineering, Beihang University, Beijing 100191, China.
Unmanned System Research Institute, Beihang University, Beijing 100191, China.
Sensors (Basel). 2021 Nov 2;21(21):7307. doi: 10.3390/s21217307.
In this paper, we investigate the problem of unmanned aerial vehicles (UAVs) autonomous tracking moving target with only an airborne camera sensor. We proposed a novel integrated controller framework for this problem based on multi-neural-network modules (MNNMs). In this framework, two neural networks are designed for target perception and guidance control, respectively. The deep learning method and reinforcement learning method are applied to train the integrated controller. The training result demonstrates that the integrated controller can be trained more quickly and efficiently than the end-to-end controller trained by the deep reinforcement learning method. The flight tests with the integrated controller are implemented in simulated and realistic environments, the results show that the integrated controller trained in simulation can easily be transferred to the realistic environment and achieve the UAV tracking randomly moving target, which has a faster motion velocity. The integrated controller based on the MNNMs structure has a better performance on an autonomous tracking target than the control mode that combines with a perception network and a proportional integral derivative controller.
在本文中，我们研究了仅使用机载相机传感器的无人机自主跟踪移动目标的问题。针对该问题，我们提出了一种基于多神经网络模块（MNNM）的新型集成控制器框架。在此框架中，分别设计了两个神经网络用于目标感知和制导控制。应用深度学习方法和强化学习方法来训练集成控制器。训练结果表明，与通过深度强化学习方法训练的端到端控制器相比，集成控制器能够更快、更高效地进行训练。使用集成控制器的飞行测试在模拟和现实环境中进行，结果表明，在模拟环境中训练的集成控制器能够轻松转移到现实环境中，并实现无人机跟踪具有更快运动速度的随机移动目标。基于MNNM结构的集成控制器在自主跟踪目标方面比结合感知网络和比例积分微分控制器的控制模式具有更好的性能。
