发表机构
University of Virginia（弗吉尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出无需训练的扩散运动规划器，用解析局部分数替代学习分数，实现复杂环境下多智能体快速生成平滑无碰撞轨迹，避免数据依赖。
AI 中文摘要
路径寻找和多机器人运动规划需要生成在具有复杂几何约束的环境中平滑、目标导向且无碰撞的轨迹。最近的基于扩散的规划器表明，轨迹生成可以被视为迭代去噪过程，这为能够处理多模态轨迹分布并细化整个轨迹的学习方法打开了大门。然而，一个关键的局限是扩散规划器需要在大量可行轨迹集合上进行训练，这使其具有地图特定性，并且在高质量演示不可用时难以部署。本文提出了一种无需训练的基于扩散的运动规划器，用源自障碍物、平滑性、速度和智能体间可行性项的解析局部分数替代学习到的全局轨迹分数。所提出的想法依赖于一个关键观察：轨迹的分数可以通过仅考虑相邻路径点与附近约束之间的局部交互来重建。这种结构利用产生了一个分解的去噪过程，保留了经典轨迹方法的优化结构，同时继承了扩散模型的迭代细化行为。在大量复杂环境和大型多智能体规划任务上的实验表明，所提出的解析分数在有限的计算成本内生成平滑且可行的轨迹，例如在GPU上不到6秒内为包含100多个障碍物的环境中的300多个智能体生成可行路径，优于强大的基于学习和优化的基线，同时避免了学习型扩散规划器的数据需求。
英文摘要
Path finding and multi-robot motion planning require trajectories that are smooth, goal-directed, and collision-free in environments with complex geometric constraints. Recent diffusion-based planners have shown that trajectory generation can be cast as iterative denoising which has opened the doors to learning-based approaches that can handle multi-modal trajectory distributions and refine entire trajectories. However, a key limitation is that diffusion planners require training on large collections of feasible trajectories, rendering them map-specific, and difficult to deploy when high-quality demonstrations are unavailable. This paper introduces a training-free diffusion-based motion planner that replaces learned global trajectory scores with analytical local scores derived from obstacle, smoothness, velocity, and inter-agent feasibility terms. The proposed idea relies on a key observation: the score of a trajectory can be reconstructed by considering only local interactions between neighboring waypoints and nearby constraints. This structure exploitation yields a decomposed denoising procedure that retains the optimization structure of classical trajectory methods while inheriting the iterative refinement behavior of diffusion models. Experiments on a large collection of complex environments and large multi-agent planning tasks show that the proposed analytical score produces smooth and feasible trajectories within limited computational costs, for example in generating feasible paths for 300+ agents in environments containing 100+ obstacles in under 6 seconds on a GPU, outperforming strong learning-based and optimization baselines, while avoiding the data requirements of learned diffusion planners.
Commentspreprint - under review
