Hybrid F' and ROS2 Architecture for Vision-Based Autonomous Flight: Design and Experimental Validation

Autonomous aerospace systems require architectures that balance deterministic real-time control with advanced perception capabilities. This paper presents an integrated system combining NASA's F' flight software framework with ROS2 middleware via Protocol Buffers bridging. We evaluate the architecture through a 32.25-minute indoor quadrotor flight test using vision-based navigation. The vision system achieved 87.19 Hz position estimation with 99.90\% data continuity and 11.47 ms mean latency, validating real-time performance requirements. All 15 ground commands executed successfully with 100 % success rate, demonstrating robust F'--PX4 integration. System resource utilization remained low (15.19 % CPU, 1,244 MB RAM) with zero stale telemetry messages, confirming efficient operation on embedded platforms. Results validate the feasibility of hybrid flight-software architectures combining certification-grade determinism with flexible autonomy for autonomous aerial vehicles.

DroneVLA: VLA based Aerial Manipulation

As aerial platforms evolve from passive observers to active manipulators, the challenge shifts toward designing intuitive interfaces that allow non-expert users to command these systems naturally. This work introduces a novel concept of autonomous aerial manipulation system capable of interpreting high-level natural language commands to retrieve objects and deliver them to a human user. The system is intended to integrate a MediaPipe based on Grounding DINO and a Vision-Language-Action (VLA) model with a custom-built drone equipped with a 1-DOF gripper and an Intel RealSense RGB-D camera. VLA performs semantic reasoning to interpret the intent of a user prompt and generates a prioritized task queue for grasping of relevant objects in the scene. Grounding DINO and dynamic A* planning algorithm are used to navigate and safely relocate the object. To ensure safe and natural interaction during the handover phase, the system employs a human-centric controller driven by MediaPipe. This module provides real-time human pose estimation, allowing the drone to employ visual servoing to maintain a stable, distinct position directly in front of the user, facilitating a comfortable handover. We demonstrate the system's efficacy through real-world experiments for localization and navigation, which resulted in a 0.164m, 0.070m, and 0.084m of max, mean euclidean, and root-mean squared errors, respectively, highlighting the feasibility of VLA for aerial manipulation operations.