AgenticDiffusion: Agentic Diffusion-based Path Planning for Vision-Based UAV Navigation
2026-06-02 • Robotics
RoboticsArtificial Intelligence
AI summaryⓘ
The authors developed AgenticDiffusion, a system for indoor drone navigation that uses views from both the drone's camera and a top-down perspective to better understand the environment. Their method uses language instructions and advanced models to identify targets and plan safe paths around obstacles. By combining multiple viewpoints, the system avoids redundant searching and makes flying inside cluttered spaces more efficient. They tested it in real-world scenarios and achieved an 80% success rate for missions and perfect trajectory planning success.
UAV navigationmulti-view observationlanguage-guided reasoningopen-vocabulary groundingdiffusion planningnonlinear model predictive control (NMPC)first-person view (FPV)trajectory executionindoor navigationtarget localization
Authors
Faryal Batool, Muhammad Ahsan Mustafa, Fawad Mehboob, Valerii Serpiva, Dzmitry Tsetserukou
Abstract
Indoor UAV navigation requires efficient exploration, scene understanding, and reliable trajectory execution under limited field-of-view observations. Existing vision-based navigation frameworks typically rely on single-view observations, limiting their ability to reason about occlusions, target visibility, and global scene structure. In this work, we propose AgenticDiffusion, a multi-view UAV navigation framework that coordinates language-guided reasoning, open-vocabulary target grounding, vision-based diffusion planning, and NMPC within a unified aerial navigation pipeline. Given a natural language instruction and synchronized first-person-view (FPV) and top-view observations, the framework determines the most informative viewpoint for navigation and generates a mission plan prior to trajectory execution. The targets are localized using an open-vocabulary grounding model, after which viewpoint-specific diffusion planners generate navigation trajectories for UAV execution. Using complementary viewpoints, the proposed framework reduces repeated target exploration and improves navigation efficiency in cluttered indoor environments. The framework was validated in four real-world UAV navigation scenarios involving adaptive viewpoint selection, multi-stage mission execution, long-horizon navigation, and safe landing-site selection. The experimental results demonstrated an overall mission success rate of 80% in 40 real-world trials, while the diffusion planners achieved a trajectory generation success rate of 100%.