用生成对抗网络预测无人机着陆轨迹，提升避障精度。
Landing Trajectory Prediction for UAS Based on Generative Adversarial Network
- 基于GAN构建生成器与判别器，学习飞行轨迹特征。
- 在2600条真实无人机着陆数据上，预测误差低于基线方法(GMR)。
- 适合关注无人机空管、智能避障的开发者与研究者。
轨迹预测模型是先进空中交通管理的关键组件，有助于飞机识别冲突并规划规避动作，尤其在靠近垂直起降机场（vertiports）的密集空域中对无人航空系统（UAS）着陆管理至关重要。本文提出一种基于生成对抗网络（GAN）的无人机着陆轨迹预测模型。GAN由生成器和判别器两个神经网络构成，利用其学习能力捕捉轨迹样本特征。生成器以历史轨迹为输入，输出未来飞行状态。实验表明，该模型在多个数据集上的预测精度优于基线方法（高斯混合回归，GMR）。为评估模型性能，我们还构建了一个真实无人机着陆数据集，包含超过2600条由真人飞行员操控的无人机着陆轨迹。
原文摘要 · Abstract (English)
Models for trajectory prediction are an essential component of many advanced air mobility studies. These models help aircraft detect conflict and plan avoidance maneuvers, which is especially important in Unmanned Aircraft systems (UAS) landing management due to the congested airspace near vertiports. In this paper, we propose a landing trajectory prediction model for UAS based on Generative Adversarial Network (GAN). The GAN is a prestigious neural network that has been developed for many years. In previous research, GAN has achieved many state-of-the-art results in many generation tasks. The GAN consists of one neural network generator and a neural network discriminator. Because of the learning capacity of the neural networks, the generator is capable to understand the features of the sample trajectory. The generator takes the previous trajectory as input and outputs some random status of a flight. According to the results of the experiences, the proposed model can output more accurate predictions than the baseline method(GMR) in various datasets. To evaluate the proposed model, we also create a real UAV landing dataset that includes more than 2600 trajectories of drone control manually by real pilots.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。