去噪多机器人轨迹
Denoising Multi-Robot Trajectories
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- University of Cambridge（剑桥大学）
- National Institute of Advanced Industrial Science and Technology (AIST)（日本产业技术综合研究所（AIST））
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中文总结 AI 辅助
本文基于动力学感知的扩散去噪框架D4orm，开发了多种多机器人轨迹规划架构，在2D/3D环境中验证其比MPPI等方法更快更可靠，并实现了真实四旋翼和地面机器人的零样本及分布式部署。
中文摘要 AI 辅助
多机器人轨迹规划是多机器人协调中的一个基本问题，但由于其非凸、多模态和高维的特性，在计算上仍然具有挑战性。本工作基于D4orm（一种动力学感知的扩散去噪框架），开发了一系列适用于不同运行需求的规划架构。与传统的数值优化方法不同，D4orm采用基于采样的优化，通过大规模并行采样生成解决方案轨迹，利用现代计算架构如GPU。其扩散去噪结构迭代优化候选控制轨迹的“变形”，为生成动力学可行且无冲突的轨迹提供了一种高效且多用途的范式。以D4orm作为高级规划器的构建模块，我们提出了一种用于提高可扩展性的解耦规划器、一种带反馈控制的在线滚动时域规划器，以及一种适用于资源受限环境的分布式规划器。在2D和3D环境中使用差速驱动机器人和全向移动机器人的评估表明，基于D4orm的方法比其他基于采样的优化方法（如MPPI）以及基于学习扩散模型的方法，能够更快、更可靠地找到高质量解决方案。我们进一步展示了在十架真实四旋翼飞行器（含障碍物）上的零样本部署、100个模拟机器人的大规模冲突消解，以及六台地面机器人的完全机载分布式“终身”运行。总体而言，这些结果确立了扩散去噪作为多机器人协调的可扩展且可靠的框架。代码和视频：此https链接。
英文摘要
Multi-robot trajectory planning is a fundamental problem in multi-robot coordination but remains computationally challenging due to its nonconvex, multimodal, and high-dimensional nature. This work builds upon D4orm, a dynamics-aware diffusion-denoising framework, and develops a family of planning architectures for diverse operational requirements. Unlike conventional numerical optimization methods, D4orm employs sampling-based optimization to generate solution trajectories through massively parallel sampling, leveraging modern computing architectures such as GPUs. Its diffusion-denoising structure iteratively optimizes \textit{deformations} to candidate control trajectories, providing an efficient and versatile paradigm for generating kinodynamically feasible and conflict-free trajectories. Using D4orm as the building block for advanced planners, we present a decoupled planner for improved scalability, an online receding-horizon planner with feedback control, and a distributed planner for resource-constrained settings. Evaluations with differential-drive and holonomic robots in 2D and 3D environments demonstrate that D4orm-based approaches find high-quality solutions faster and more reliably than other sampling-based optimization methods, such as MPPI, as well as a learned diffusion-model-based method. We further demonstrate zero-shot deployment on ten real quadrotors with obstacles, large-scale deconfliction with 100 simulated robots, and fully onboard distributed `lifelong' operation with six ground robots. Overall, these results establish diffusion denoising as a scalable and reliable framework for multi-robot coordination. Code and video: https://github.com/proroklab/d4orm
