Control-Aware Trajectory Prediction for Communication-Free Drone Swarm Coordination in Cluttered Environments

Swarms of Unmanned Aerial Vehicles (UAVs) 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, particularly where communication is not available. To address this challenge, we propose a control-aware learning-based trajectory prediction algorithm that can enable communication-free 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 without communication. 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, 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. The results show that the proposed algorithm outperforms state-of-the-art benchmarks, providing close-to-optimal control performance even when communication is unavailable.