  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.
