用模拟预测邻居动作，让机器人在复杂环境里不撞车且少通信。
Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning
- 用约束扩散模型先模拟邻居未来轨迹
- 108个机器人+160障碍物下仍能有效避障
- 仅在拥挤时触发通信，适合大规模多机协同
去中心化多机器人路径规划要求每个机器人仅基于局部观测生成无碰撞轨迹，无需全局感知或可靠通信。然而，现有方法（无论经典或学习型）通常仅从局部观测的静态快照生成轨迹，难以预判邻近机器人的未来行为，这一缺陷在机器人数量增多和环境更密集时尤为严重。为此，本文提出仿真引导扩散（Simulation-Informed Diffusion, SID），基于约束感知扩散模型（CADM）构建去中心化框架。SID首先利用CADM从当前观测状态模拟邻近机器人的未来轨迹，随后使用相同CADM在安全约束下规划自身轨迹，约束来源于对邻居行为的模拟。关键在于，对邻居行为的精准模拟使得通信仅在高拥堵场景下必要时触发，实现最小化通信开销。在多种环境下的实验表明，SID在规划有效性与约束满足率方面均优于基线方法，并可扩展至包含108个机器人和160个障碍物的复杂场景。
原文摘要 · Abstract (English)
Decentralized multi-robot motion planning requires each robot to generate collision-free trajectories from local observations, without global sensing or reliable communication. However, most existing planners, whether classical or learning-based, generate trajectories from a static snapshot of the local observation, which limits their ability to anticipate the future behavior of neighboring robots. This limitation is critical as the number of robots increases and the environment becomes more cluttered. To overcome this challenge, this paper introduces Simulation-Informed Diffusion (SID), a decentralized framework built on constraint-aware diffusion models (CADM). SID first uses CADM to simulate the future trajectories of neighboring robots from their currently observed states, and then uses the same CADM to plan each robot's own trajectory under safety constraints informed by these simulations. Crucially, the accurate simulation of neighbors enables a minimal communication scheme that triggers coordination only when necessary in highly congested scenarios. Experiments across diverse environments show that SID consistently outperforms baseline methods in terms of planning effectiveness and constraint satisfaction, and scales to scenarios with 108 robots and 160 obstacles.
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