Multi-Agent Cooperative Navigation Algorithm Based on Trajectory Optimization with Global Structural Constraints and Attention-Enhanced Graph Neural Networks

To address the challenges of formation maintenance, collision avoidance, and cooperative navigation for multi-agent systems in shared environments, a spatio-temporal trajectory optimization method under global structural constraints and A attention-enhanced graph neural network (AE-GNN)-based cooperative navigation strategy are proposed. First, a differentiable formation similarity metric based on graph Laplacian theory is constructed to characterize the intrinsic geometric structure of formations, which is invariant to translation, rotation, and scale transformations. Based on this metric, a distributed trajectory optimization framework is developed by jointly considering formation maintenance, obstacle avoidance, inter-agent collision avoidance, and trajectory smoothness. Furthermore, an adaptive formation constraint adjustment mechanism based on environmental passage margin is introduced, allowing agents to relax rigid formation constraints in narrow passages and gradually recover the desired formation after obstacle avoidance. To improve cooperative decision-making in densely cluttered environments, an AE-GNN navigation method is proposed, where an attention mechanism dynamically assigns higher weights to critical neighboring agents during feature aggregation, enhancing cooperative information interaction and collision avoidance capability. Simulation and physical experiments validate the effectiveness of both proposed methods. The trajectory optimization method achieves a success rate of 97.5% in dense-obstacle formation-navigation simulations, while AE-GNN demonstrates effective cooperative navigation and collision avoidance in multi-agent scenarios.