SwarmDiff: Swarm Robotic Trajectory Planning in Cluttered Environments via Diffusion Transformer

Kang Ding, Chunxuan Jiao, Yunze Hu, Kangjie Zhou, Pengying Wu, Yao Mu, Chang Liu; Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR) Workshops, 2025, pp. 4164-4173

Abstract


Swarm robotic trajectory planning faces challenges in 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 refinement of individual robot's 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.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Ding_2025_CVPR, author = {Ding, Kang and Jiao, Chunxuan and Hu, Yunze and Zhou, Kangjie and Wu, Pengying and Mu, Yao and Liu, Chang}, title = {SwarmDiff: Swarm Robotic Trajectory Planning in Cluttered Environments via Diffusion Transformer}, booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR) Workshops}, month = {June}, year = {2025}, pages = {4164-4173} }