面向杂乱环境中通信高效无人机集群协调的控制感知轨迹预测
Control-Aware Trajectory Predictions for Communication-Efficient Drone Swarm Coordination in Cluttered Environments
AI总结：
针对复杂环境下无人机集群通信受限问题，提出融合 EvolveGCN 轨迹预测与 KKT 条件训练的控制感知方法，为 DMPC 提供邻居轨迹预测，实现近优且鲁棒的通信高效协同控制。
摘要：
无人机（UAV）集群已在众多工业和商业应用中展现出巨大潜力。然而，在实际部署无人机之前，必须确保它们能够在复杂环境中安全运行，尤其是在通信能力有限的情况下。为应对这一挑战，我们提出一种控制感知的、基于学习的轨迹预测算法，可在杂乱环境中实现通信高效的无人机集群控制。具体而言，该算法能够使每架无人机在具有不同通信能力水平的场景中预测其邻居的规划轨迹。预测得到的规划轨迹将作为分布式模型预测控制（DMPC）方法的输入。所提算法结合了：（1）基于 EvolveGCN 的轨迹预测模型，EvolveGCN 是一种可处理动态图的图卷积网络（GCN），并通过来自相邻无人机的压缩消息得到进一步增强；（2）KKT 引导的训练方法，在训练过程中应用 Karush-Kuhn-Tucker（KKT）条件，将 DMPC 信息编码到训练后的神经网络中。我们在漏斗状环境中评估了所提算法。结果表明，该算法优于最先进的基准方法，可提供接近最优的控制性能，并对有限通信能力和测量噪声具有鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
