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

2026-03-09

AI Summary

This work addresses the challenge of long-horizon safe navigation for drone swarms in complex indoor environments by proposing a hierarchical framework that integrates an image-conditioned diffusion model with variable impedance control. At the high level, a diffusion model generates global trajectories end-to-end from RGB images; at the mid level, reactive trajectory tracking is achieved via an artificial potential field method; and at the low level, semantic-aware variable impedance control adapts to heterogeneous obstacles. The approach innovatively combines diffusion models with impedance control for the first time and incorporates vision-language model retrieval-augmented generation (VLM-RAG) to enhance environmental understanding. Evaluated on the Crazyflie 2.1 platform across 20 dynamic and static scenarios with 100 trials, the system achieves a 92% success rate without collisions while maintaining stable formation, with the FPV planner reaching speeds up to 2.0 m/s, demonstrating its efficiency and robustness.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• UAV swarm navigation
• cluttered indoor environment
• global path planning
• obstacle avoidance
• adaptive impedance control

Innovation

• diffusion-based planning
• impedance control
• semantic-aware navigation
• UAV swarm
• zero-shot sim-to-real