DISCO: Diffusion-based Inter-Agent Swarm Collision-free Optimization for UAVs

Bitla Bhanu Teja¹    Simon Idoko²    T. Shilpitha Chowdary³    K Madhava Krishna¹    Arun Kumar Singh²   

¹ Robotics Research Center, IIIT Hyderabad, India    ² University of Tartu, Estonia   

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 de-noising 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