Swarm robotic trajectory planning faces challenges in computational efficiency, scalability, and safety, particularly in complex, obstacle-dense environments. To address these issues, we propose SwarmDiff, a hierarchical and scalable generative framework for swarm robots. We model the swarm's macroscopic state using Probability Density Functions (PDFs) and leverage conditional diffusion models to generate risk-aware macroscopic trajectory distributions, which then guide the generation of individual robot trajectories at the microscopic level. To ensure a balance between the swarm's optimal transportation and risk awareness, we integrate Wasserstein metrics and Conditional Value at Risk (CVaR). Additionally, we introduce a Diffusion Transformer (DiT) to improve sampling efficiency and generation quality by capturing long-range dependencies. Extensive simulations and real-world experiments demonstrate that SwarmDiff outperforms existing methods in computational efficiency, trajectory validity, and scalability, making it a reliable solution for swarm robotic trajectory planning.
View on arXiv@article{ding2025_2505.15679,
title={ SwarmDiff: Swarm Robotic Trajectory Planning in Cluttered Environments via Diffusion Transformer },
author={ Kang Ding and Chunxuan Jiao and Yunze Hu and Kangjie Zhou and Pengying Wu and Yao Mu and Chang Liu },
journal={arXiv preprint arXiv:2505.15679},
year={ 2025 }
}