Swarms of Unmanned Aerial Vehicles (UAV) have demonstrated enormous potential in many industrial and commercial applications. However, before deploying UAVs in the real world, it is essential to ensure they can operate safely in complex environments, especially with limited communication capabilities. To address this challenge, we propose a control-aware learning-based trajectory prediction algorithm that can enable communication-efficient UAV swarm control in a cluttered environment. Specifically, our proposed algorithm can enable each UAV to predict the planned trajectories of its neighbors in scenarios with various levels of communication capabilities. The predicted planned trajectories will serve as input to a distributed model predictive control (DMPC) approach. The proposed algorithm combines (1) a trajectory compression and reconstruction model based on Variational Auto-Encoder, (2) a trajectory prediction model based on EvolveGCN, a graph convolutional network (GCN) that can handle dynamic graphs, and (3) a KKT-informed training approach that applies the Karush-Kuhn-Tucker (KKT) conditions in the training process to encode DMPC information into the trained neural network. We evaluate our proposed algorithm in a funnel-like environment. Results show that the proposed algorithm outperforms state-of-the-art benchmarks, providing close-to-optimal control performance and robustness to limited communication capabilities and measurement noises.

翻译：无人飞行器（UAV）集群已在众多工业和商业应用中展现出巨大潜力。然而，在将无人机部署于现实世界前，必须确保其能在复杂环境中安全运行，尤其是在通信能力受限的条件下。为应对这一挑战，我们提出了一种基于控制感知的学习型轨迹预测算法，该算法能在复杂环境中实现通信高效的无人机集群控制。具体而言，该算法使每架无人机能够在不同通信能力水平下预测其邻居的规划轨迹，并将预测结果输入分布式模型预测控制（DMPC）框架。所提算法融合了：（1）基于变分自编码器的轨迹压缩与重构模型；（2）基于EvolveGCN（一种能处理动态图的图卷积网络）的轨迹预测模型；以及（3）基于KKT条件的训练方法——该方法在训练过程中应用库恩-塔克（KKT）条件，将DMPC信息编码至训练完成的神经网络中。我们在漏斗形环境中对所提算法进行了评估。结果表明，该算法优于现有基准方法，在通信能力受限及存在测量噪声的条件下，能提供接近最优的控制性能与鲁棒性。