发表机构
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出FlockDiffusion，一种结合场景图编码器、分配头、条件扩散变压器和闭式轨迹解码器的学习框架，通过任务捆绑和教师监督，实现多无人机高效任务分配与路径规划，显著提升完成率并降低路线成本与推理延迟。
AI 中文摘要
自主多无人机导航要求机群在紧凑的计算预算下，于杂乱环境中服务分布式目标。高效协调依赖于任务捆绑，即每架无人机沿其路线访问多个目标。分别用于成本估计、分配和执行的求解器会引发冗余的图搜索，并产生长而突兀的路径。我们提出FlockDiffusion，一个结合场景图编码器、显式分配头、分配条件扩散变压器和闭式轨迹解码器的学习框架。自回归教师提供离线监督，用于并行机群轨迹生成。PyBullet消融实验表明，捆绑将任务完成率从50%提升至100%，而我们的完整教师进一步将路线成本相对于带捆绑的MAGNNET降低8.4%。在优化的可扩展性基准中，在每种密度100个场景、10架无人机下评估，FlockDiffusion比经典流程实现6.2至7.6倍更快的推理速度和约37%更短的路线。当名义任务数从20增加到40时，延迟从7.8毫秒升至11.1毫秒，而基线为48.0至75.8毫秒。在跨五个Gazebo环境的单独评估中，FlockDiffusion实现100%规划器覆盖率，并将规划路线成本相对于带捆绑的基线降低15.4%。这些结果证明了在难以用物理无人机机群复现的配置中，随着任务密度增加的高效规划能力。
英文摘要
Autonomous multi-drone navigation requires fleets to service distributed objectives in cluttered environments under tight computational budgets. Efficient coordination depends on task bundling, where each drone visits multiple objectives along its route. Separate solvers for cost estimation, assignment, and execution incur redundant graph search and produce long, abrupt paths. We propose FlockDiffusion, a learned framework combining a scene graph encoder, an explicit allocation head, an assignment conditioned diffusion transformer, and a closed form trajectory decoder. An autoregressive teacher provides offline supervision for parallel fleet trajectory generation. PyBullet ablations show that bundling increases task completion from 50% to 100%, while our complete teacher further reduces route cost by 8.4% relative to MAGNNET with bundling. In the optimized scalability benchmark, evaluated on 100 scenes per density with ten drones, FlockDiffusion achieves 6.2 to 7.6 times faster inference and approximately 37% shorter routes than the classical pipeline. As nominal task counts increase from 20 to 40, latency rises from 7.8 to 11.1 ms, compared with 48.0 to 75.8 ms for the baseline. In a separate evaluation across five Gazebo environments, FlockDiffusion achieves 100% planner coverage and reduces planned route cost by 15.4% relative to the baseline with bundling. These results demonstrate efficient planning under increasing task density in configurations that are demanding to reproduce with physical drone fleets.
