This paper presents a vision-based modularized drone racing navigation system that uses a customized convolutional neural network (CNN) for the perception module to produce high-level navigation commands and then leverages a state-of-the-art planner and controller to generate low-level control commands, thus exploiting the advantages of both data-based and model-based approaches. Unlike the state-of-the-art method which only takes the current camera image as the CNN input, we further add the latest three drone states as part of the inputs. Our method outperforms the state-of-the-art method in various track layouts and offers two switchable navigation behaviors with a single trained network. The CNN-based perception module is trained to imitate an expert policy that automatically generates ground truth navigation commands based on the pre-computed global trajectories. Owing to the extensive randomization and our modified dataset aggregation (DAgger) policy during data collection, our navigation system, which is purely trained in simulation with synthetic textures, successfully operates in environments with randomly-chosen photorealistic textures without further fine-tuning.
翻译:本文展示了一个基于愿景的模块化无人机赛车导航系统,该系统使用定制的进化神经神经网络(CNN)来生成高级导航指令,然后利用最先进的规划师和控制师来生成低级控制指令,从而利用基于数据和基于模型的方法的优势。与仅将当前相机图像作为CNN输入的先进方法不同,我们进一步添加了最新的三个无人机状态作为投入的一部分。我们的方法超越了各种轨道布局中最先进的导航方法,并提供了一个单一培训网络的两种可转换的导航行为。基于CNN的感知模块经过培训,可以模仿一项专家政策,该政策自动生成基于预先制作的全球轨迹的地面真象导航指令。由于数据收集期间的广泛随机化和我们经过修改的数据集汇总(Dagger)政策,我们的导航系统在模拟合成质素时经过纯经培训,在随机和摄影真实性文字的环境下成功运行,没有进一步的微调。