DISCO: Diffusion-Based Inter-Agent Swarm Collision-Free Optimization for UAVs

We present a diffusion-based generative model for coordinated trajectory planning in multi-UAV swarms. The proposed method represents each UAV's trajectory in a Bernstein polynomial coefficient space and employs a denoising diffusion process with self-attention layers to generate diverse, feasible motion plans. A safety filter is integrated into the generation pipeline to refine candidate trajectories, enforcing inter-drone collision avoidance and other feasibility constraints. The model is trained offline on a large set of expert demonstration trajectories, eliminating the need for reinforcement learning and manual reward function design. In experiments with a 16-UAV swarm using a dataset of collision-free trajectories, the approach achieved a high success rate in producing safe and smooth flight paths. These results demonstrate that the learned planner can rapidly generate a diverse set of smooth, collision-free trajectories for the swarm.