用扩散模型生成无人机群路径，实时避障且自适应调整柔性控制。
ImpedanceDiffusion: Diffusion-Based Global Path Planning for UAV Swarm Navigation with Generative Impedance Control
- 基于图像的扩散模型直接生成全局路径，无需构建地图。
- 20组实验中路径生成率100%，零样本部署成功率达92%。
- 适合复杂室内环境下的无人机集群导航与动态障碍应对。
在杂乱室内环境中实现安全无人机群导航需具备长时程规划、实时避障和自适应柔顺性。本文提出ImpedanceDiffusion，一种分层框架，利用图像条件扩散模型进行全局路径规划，结合人工势场（APF）追踪与视觉语言模型-检索增强生成（VLM-RAG）模块实现语义障碍分类（90%检索准确率），动态调节阻抗参数以适应混合障碍环境。评估两种扩散规划器：(i) 单次推理的俯视图长时程规划器；(ii) 两阶段推理的首人称视角短时程规划器。两者在20组静态与动态配置中均实现100%路径生成率，并通过零样本模拟到真实部署验证于Crazyflie 2.1无人机。俯视图规划器生成更平滑轨迹，近硬障碍物时跟踪速度为1.0–1.2 m/s，近软障碍物为0.6–1.0 m/s；首人称视角规划器局部间隙更大，速度更高，近硬障碍物达1.4–2.0 m/s，近软障碍物可达1.6 m/s。20组实验共100次运行中，系统成功率92%，保持稳定阻抗式编队控制，振荡有界，无空中碰撞，验证了在复杂室内环境中的可靠自适应群导航能力。
原文摘要 · Abstract (English)
Safe swarm navigation in cluttered indoor environment requires long-horizon planning, reactive obstacle avoidance, and adaptive compliance. We propose ImpedanceDiffusion, a hierarchical framework that leverages image-conditioned diffusion-based global path planning with Artificial Potential Field (APF) tracking and semantic-aware variable impedance control for aerial drone swarms. The diffusion model generates geometric global trajectories directly from RGB images without explicit map construction. These trajectories are tracked by an APF-based reactive layer, while a VLM-RAG module performs semantic obstacle classification with 90% retrieval accuracy to adapt impedance parameters for mixed obstacle environments during execution. Two diffusion planners are evaluated: (i) a top-view long-horizon planner using single-pass inference and (ii) a first-person-view (FPV) short-horizon planner deployed via a two-stage inference pipeline. Both planners achieve a 100% trajectory generation rate across twenty static and dynamic experimental configurations and are validated via zero-shot sim-to-real deployment on Crazyflie 2.1 drones through the hierarchical APF-impedance control stack. The top-view planner produces smoother trajectories that yield conservative tracking speeds of 1.0-1.2 m/s near hard obstacles and 0.6-1.0 m/s near soft obstacles. In contrast, the FPV planner generates trajectories with greater local clearance and typically higher speeds, reaching 1.4-2.0 m/s near hard obstacles and up to 1.6 m/s near soft obstacles. Across 20 experimental configurations (100 total runs), the framework achieved a 92% success rate while maintaining stable impedance-based formation control with bounded oscillations and no in-flight collisions, demonstrating reliable and adaptive swarm navigation in cluttered indoor environments.
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