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 prediction model based on EvolveGCN, a Graph Convolutional Network (GCN) that can handle dynamic graphs, which is further enhanced by compressed messages from adjacent UAVs, and (2) 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：无人机集群已在众多工业和商业应用中展现出巨大潜力。然而，在实际部署无人机之前，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情况下。为应对这一挑战，我们提出一种基于学习的控制感知轨迹预测算法，该算法能够在杂乱环境中实现通信高效的无人机集群控制。具体而言，所提算法能使每架无人机在不同通信能力水平的场景中预测其邻近无人机的规划轨迹。预测的规划轨迹将作为分布式模型预测控制（DMPC）方法的输入。该算法融合了：（1）基于EvolveGCN的轨迹预测模型（EvolveGCN是一种能处理动态图的图卷积网络GCN），并通过来自邻近无人机的压缩消息进一步增强；（2）一种KKT感知训练方法，该方法在训练过程中应用Karush-Kuhn-Tucker（KKT）条件，将DMPC信息编码至训练后的神经网络中。我们在漏斗状环境中对所提算法进行评估。结果表明，该算法优于现有先进基准方法，在通信能力受限和存在测量噪声的情况下，能提供接近最优的控制性能与鲁棒性。