Abstract
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.
Abstract (translated)
在拥挤的室内环境中进行安全的无人机群导航需要长期规划、反应式避障和自适应顺应控制。我们提出了ImpedanceDiffusion，这是一种分层框架，它结合了基于图像条件扩散模型的全局路径规划以及人工势场（APF）跟踪与语义感知变阻抗控制，专门用于空中无人机群。 该扩散模型可以直接从RGB图像生成几何全局轨迹，无需显式构建地图。这些轨迹由一个基于APF的反应层进行追踪，同时VLM-RAG模块执行语义障碍分类，其检索准确率为90%，并根据混合障碍环境在执行过程中调整阻抗参数。 我们评估了两种扩散规划器：（i）一种俯视视角长期规划器，采用单次推理；以及（ii）一种第一人称视角（FPV）短期规划器，通过两阶段推理管道部署。这两种规划器在二十种静态和动态实验配置中均实现了100%的轨迹生成率，并且通过分层APF-阻抗控制堆栈进行了零样本仿真到现实转换，在Crazyflie 2.1无人机上得到了验证。 俯视视角规划器产生了更平滑的轨迹，当接近硬障碍物时，其保守跟踪速度为1.0至1.2米/秒；当接近软障碍物时，则为0.6至1.0米/秒。相比之下，FPV规划器生成了具有更大局部安全距离的路径，并通常以更高的速度运行，在接近硬障碍物时达到1.4到2.0米/秒，在接近软障碍物时则高达1.6米/秒。 在二十种实验配置（总计一百次试验）中，该框架实现了92%的成功率，并保持了基于阻抗的稳定编队控制和有界振荡，且没有发生空中碰撞。这证明了它能够可靠并适应性地进行拥挤室内环境中的群组导航。
URL
https://arxiv.org/abs/2603.09031
