ImpedanceDiffusion: Diffusion-Based Global Path Planning for UAV Swarm Navigation with Generative Impedance Control

TL;DR

ImpedanceDiffusion, a hierarchical framework that leverages image-conditioned diffusion-based global path planning with Artificial Potential Field tracking and semantic-aware variable impedance control for aerial drone swarms, is proposed, demonstrating reliable and adaptive swarm navigation in cluttered indoor environments.

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.

Figures (6)

• Figure 1: ImpedanceDiffusion in a mixed hard (rigid poles) and soft (human) obstacle environment. Diffusion-based planning generates the global path, APF ensures reactive avoidance (red dashed), and adaptive impedance links maintain formation cohesion and compliant interaction during flight.
• Figure 2: System architecture of ImpedanceDiffusion. A top-view image and user prompt are processed by Molmo to identify obstacle type. The VLM--RAG module retrieves the corresponding impedance parameters from a custom database. In parallel, the image is provided to the diffusion model to generate a global trajectory, which is executed using APF-based tracking with adaptive inter-drone and drone–obstacle impedance control.
• Figure 3: Static environment with hard obstacles and a gate. Rigid compliance with small deflection is observed near hard objects.
• Figure 4: Static cluttered environment with hard obstacles and one human. Soft compliance near the human results in larger deflection and reduced speed.
• Figure 5: Dynamic environment with one moving human and one hard obstacle. Soft compliance near the human and rigid compliance near the chair are observed.