用扩散模型同时规划多机器人路径，确保不碰撞且符合运动约束。
Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models
- 将约束优化嵌入扩散采样过程，生成无碰撞可行轨迹。
- 在复杂场景中成功率显著高于传统与学习类方法。
- 提供新基准测试不同密度和障碍下的多机协同规划。
扩散模型在机器人领域展现出巨大潜力，可直接从环境原始表示生成多样且平滑的轨迹。然而，将其应用于运动规划仍面临挑战，尤其难以保证碰撞规避和运动学可行性等关键约束。这一问题在多机器人运动规划（MRMP）中尤为突出，因多个机器人需在共享空间中协调行动。为此，本文提出一种新方法SMD，将约束优化融入扩散采样过程，生成无碰撞、运动学可行的轨迹。此外，论文构建了一个涵盖不同机器人密度、障碍复杂度和运动约束的综合性MRMP基准测试集。实验表明，SMD在复杂多机器人环境中持续优于经典及其它学习型规划器，具有更高的成功率和效率。
原文摘要 · Abstract (English)
Recent advances in diffusion models hold significant potential in robotics, enabling the generation of diverse and smooth trajectories directly from raw representations of the environment. Despite this promise, applying diffusion models to motion planning remains challenging due to their difficulty in enforcing critical constraints, such as collision avoidance and kinematic feasibility. These limitations become even more pronounced in Multi-Robot Motion Planning (MRMP), where multiple robots must coordinate in shared spaces. To address these challenges, this work proposes Simultaneous MRMP Diffusion (SMD), a novel approach integrating constrained optimization into the diffusion sampling process to produce collision-free, kinematically feasible trajectories. Additionally, the paper introduces a comprehensive MRMP benchmark to evaluate trajectory planning algorithms across scenarios with varying robot densities, obstacle complexities, and motion constraints. Experimental results show SMD consistently outperforms classical and other learning-based motion planners, achieving higher success rates and efficiency in complex multi-robot environments.
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