SwarmDiff: Swarm Robotic Trajectory Planning in Cluttered Environments via Diffusion Transformer
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916749784383488 |
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| author | Ding, Kang Jiao, Chunxuan Hu, Yunze Zhou, Kangjie Wu, Pengying Mu, Yao Liu, Chang |
| author_facet | Ding, Kang Jiao, Chunxuan Hu, Yunze Zhou, Kangjie Wu, Pengying Mu, Yao Liu, Chang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_15679 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SwarmDiff: Swarm Robotic Trajectory Planning in Cluttered Environments via Diffusion Transformer Ding, Kang Jiao, Chunxuan Hu, Yunze Zhou, Kangjie Wu, Pengying Mu, Yao Liu, Chang Robotics Multiagent Systems 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. |
| title | SwarmDiff: Swarm Robotic Trajectory Planning in Cluttered Environments via Diffusion Transformer |
| topic | Robotics Multiagent Systems |
| url | https://arxiv.org/abs/2505.15679 |