用扩散模型生成群机器人安全轨迹，兼顾效率与避障。
SwarmDiff: Swarm Robotic Trajectory Planning in Cluttered Environments via Diffusion Transformer
- 用概率密度函数建模群体状态，通过条件扩散生成安全轨迹分布。
- 在复杂环境中实现更高计算效率与轨迹有效性，实测性能优于现有方法。
- 适合大规模群机器人系统，尤其适用于高密度障碍场景规划。
群机器人轨迹规划在复杂、障碍密集环境中面临计算效率低、可扩展性差和安全性不足的挑战。为此，本文提出SwarmDiff，一种分层且可扩展的生成式框架。通过概率密度函数（PDF）建模群体宏观状态，并利用条件扩散模型生成考虑风险的宏观轨迹分布，进而指导微观层面个体机器人的轨迹生成。为平衡群体运输效率与风险规避，引入沃尔什度量（Wasserstein）与条件风险价值（CVaR）。此外，采用扩散Transformer（DiT）捕捉长程依赖关系，提升采样效率与生成质量。大量仿真与真实实验表明，SwarmDiff在计算效率、轨迹有效性及可扩展性方面均优于现有方法，为群机器人轨迹规划提供可靠解决方案。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。