用扩散模型生成多智能体编队轨迹，实现平滑避障与精准跟踪。
Multi-Agent Formation Navigation Using Diffusion-Based Trajectory Generation
- 以扩散模型生成双领导者中点轨迹，定义编队运动路径。
- 仿真显示轨迹平滑、跟踪误差低，窄空间和未见障碍易失败。
- 适合复杂环境下的多机器人协同导航，尤其关注编队稳定性。
本文提出一种基于扩散模型的规划器，用于在复杂环境中实现领导者-跟随者型多智能体编队控制。该方法利用扩散策略生成两个领导者中点的轨迹，将其视为平面内的刚性杆，从而定义其期望运动路径。跟随者仅依赖自身局部坐标中的相对位置，通过距离约束的编队控制器跟踪领导者，形成期望的几何构型。所提方法生成平滑轨迹并实现低跟踪误差；主要失败场景出现在狭窄无障碍区域或训练数据中未出现的障碍配置。仿真结果表明，扩散模型在可靠多智能体编队规划中具有潜力。
原文摘要 · Abstract (English)
This paper introduces a diffusion-based planner for leader--follower formation control in cluttered environments. The diffusion policy is used to generate the trajectory of the midpoint of two leaders as a rigid bar in the plane, thereby defining their desired motion paths in a planar formation. While the followers track the leaders and form desired foramtion geometry using a distance-constrained formation controller based only on the relative positions in followers' local coordinates. The proposed approach produces smooth motions and low tracking errors, with most failures occurring in narrow obstacle-free space, or obstacle configurations that are not in the training data set. Simulation results demonstrate the potential of diffusion models for reliable multi-agent formation planning.
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