阻抗扩散：基于扩散的全局路径规划用于无人机群导航的生成阻抗控制
ImpedanceDiffusion: Diffusion-Based Global Path Planning for UAV Swarm Navigation with Generative Impedance Control
AI总结：
ImpedanceDiffusion通过结合扩散模型和人工势场跟踪，实现无人机群在复杂环境中的可靠路径规划与自适应阻抗控制。
摘要：
在复杂的室内环境中实现安全的群体导航需要长视距规划、反应性障碍物避让和自适应合规性。我们提出了ImpedanceDiffusion，一种分层框架，该框架利用基于图像的扩散式全局路径规划结合人工势场（APF）跟踪和语义感知的可变阻抗控制，用于空中无人机群。扩散模型能够直接从RGB图像生成几何全局轨迹，而无需显式地图构建。这些轨迹由基于APF的反应层进行跟踪，同时VLM-RAG模块通过90%的检索准确率进行语义障碍物分类，以适应执行期间的混合障碍物环境中的阻抗参数。两个扩散规划器被评估：（i）一个俯视长视距规划器使用单次推理，（ii）一个第一人称视图（FPV）短视距规划器通过两阶段推理管道部署。这两种规划器在二十个静态和动态实验配置中均实现了100%的轨迹生成率，并通过零样本sim-to-real部署在Crazyflie 2.1无人机上进行了验证，通过分层APF-阻抗控制堆栈。俯视规划器生成更平滑的轨迹，在硬障碍物附近产生保守的跟踪速度1.0-1.2 m/s，在软障碍物附近为0.6-1.0 m/s。相比之下，FPV规划器生成的轨迹具有更大的局部 clearance 和通常更高的速度，在硬障碍物附近达到1.4-2.0 m/s，在软障碍物附近可达到1.6 m/s。在20个实验配置（100次总运行）中，该框架实现了92%的成功率，同时保持稳定的基于阻抗的编队控制，具有有界的振荡和无飞行碰撞，展示了在复杂室内环境中可靠和自适应的群体导航。
英文摘要：
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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