SwarmDiff：基于扩散Transformer的杂乱环境中群体机器人轨迹规划
SwarmDiff: Swarm Robotic Trajectory Planning in Cluttered Environments via Diffusion Transformer
AI总结：
针对杂乱环境中群体机器人轨迹规划的效率、扩展性与安全挑战，提出分层生成框架SwarmDiff，结合PDF建模、条件扩散、CVaR和DiT实现高效安全的轨迹规划，性能优于现有方法。
摘要：
群体机器人轨迹规划在计算效率、可扩展性和安全性方面面临挑战，在复杂、障碍物密集的环境中尤为突出。为解决这些问题，我们提出SwarmDiff，一种面向群体机器人的分层可扩展生成框架。我们使用概率密度函数（PDFs）对群体的宏观状态建模，利用条件扩散模型生成具备风险感知的宏观轨迹分布，再在微观层面引导单机器人轨迹的生成。为平衡群体的最优运输与风险感知，我们整合了Wasserstein度量和条件风险价值（CVaR）。此外，我们引入扩散Transformer（DiT），通过捕捉长距离依赖提升采样效率和生成质量。大量仿真与真实世界实验表明，SwarmDiff在计算效率、轨迹有效性和可扩展性上优于现有方法，是群体机器人轨迹规划的可靠方案。
英文摘要：
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.
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