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

• URL: http://arxiv.org/abs/2602.15398v1
• Date: Tue, 17 Feb 2026 07:12:06 GMT
• Title: Hybrid F' and ROS2 Architecture for Vision-Based Autonomous Flight: Design and Experimental Validation
• Abstract summary: This paper presents an integrated system combining NASA's F' flight software framework with ROS2 via Protocol Buffers bridging.<n>We evaluate the architecture through a 32.25-minute indoor quadrotor flight test using vision-based navigation.
• Score: 2.8845728740932794
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• FlowATC: Aircraft Trajectory Prediction via Flow Matching [39.046509366232065]
  We present a flow-matching architecture trained exclusively on historical aircraft trajectories, with no route labels or chart supervision.<n>Our model is trained directly on the native, irregular ADS-B sampling interval.<n>We show that the error degrades gracefully with prediction horizon, and the architecture remains effective when retrained on temporally decimated feeds.
  arXiv  Detail & Related papers  (2026-09-15T02:21:06Z)
• MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Research [39.68394140712043]
  MobileGym is a lightweight, fully controllable environment for everyday mobile use.<n>A layered state model and a declarative task-definition framework keep state programmability and task creation practical at scale.<n>A single programmatic judging mechanism delivers both deterministic evaluation verdicts and dense rewards.
  arXiv  Detail & Related papers  (2026-05-25T17:59:49Z)
• Instance-level Visual Active Tracking with Occlusion-Aware Planning [61.982298426203165]
  Visual Active Tracking (VAT) aims to control cameras to follow a target in 3D space.<n>VAT faces two key bottlenecks in real-world deployment: confusion from visually similar distractors and severe failure under occlusions.<n>We propose OA-VAT, a unified pipeline with three complementary modules.
  arXiv  Detail & Related papers  (2026-04-23T09:11:50Z)
• Dual Pose-Graph Semantic Localization for Vision-Based Autonomous Drone Racing [0.0]
  Existing visual SLAM systems struggle with motion blur and feature instability inherent to racing dynamics.<n>We present a dual pose-graph architecture that fuses odometry with semantic detections for robust localization.
  arXiv  Detail & Related papers  (2026-04-16T15:47:15Z)
• Omni-SimpleMem: Autoresearch-Guided Discovery of Lifelong Multimodal Agent Memory [76.63021613850093]
  We deploy an autonomous research pipeline to discover Omni-SimpleMem, a unified multimodal memory framework for lifelong AI agents.<n>System achieves state-of-the-art on two benchmarks, improving F1 by +411% on LoCoMo and +214% on Mem-Gallery.<n>We provide a taxonomy of six discovery types and identify four properties that make multimodal memory particularly suited for autoresearch.
  arXiv  Detail & Related papers  (2026-04-01T15:06:23Z)
• A Unified Experimental Architecture for Informative Path Planning: from Simulation to Deployment with GuadalPlanner [69.43049144653882]
  This paper introduces a unified architecture that decouples high-level decision-making from vehicle-specific control.<n>The proposed architecture is realized through GuadalPlanner, which defines standardized interfaces between planning, sensing, and vehicle execution.
  arXiv  Detail & Related papers  (2026-02-11T10:02:31Z)
• aerial-autonomy-stack -- a Faster-than-real-time, Autopilot-agnostic, ROS2 Framework to Simulate and Deploy Perception-based Drones [0.3262230127283452]
  We introduce aerial-autonomy-stack, an open-source, end-to-end framework designed to streamline the pipeline from ( GPU-accelerated) perception to (flight controller-based) action.<n>We show that it supports over 20x faster-than-real-time, end-to-end simulation of a complete development and deployment stack.
  arXiv  Detail & Related papers  (2026-02-06T23:29:33Z)
• Self-Supervised Learning to Fly using Efficient Semantic Segmentation and Metric Depth Estimation for Low-Cost Autonomous UAVs [5.602128292727329]
  This paper presents a vision-only autonomous flight system for small UAVs operating in controlled indoor environments.<n>The system combines semantic segmentation with monocular depth estimation to enable obstacle avoidance, scene exploration, and autonomous safe landing operations.<n>A key innovation is an adaptive scale factor algorithm that converts non-metric monocular depth predictions into accurate metric distance measurements.
  arXiv  Detail & Related papers  (2025-10-18T19:35:17Z)
• ASTREA: Introducing Agentic Intelligence for Orbital Thermal Autonomy [51.56484100374058]
  ASTREA is the first agentic system executed on flight-heritage hardware for autonomous spacecraft operations.<n>We integrate a resource-constrained Large Language Model (LLM) agent with a reinforcement learning controller in an asynchronous architecture tailored for space-qualified platforms.
  arXiv  Detail & Related papers  (2025-09-16T08:52:13Z)
• UAV Individual Identification via Distilled RF Fingerprints-Based LLM in ISAC Networks [60.16924915676577]
  Unmanned aerial vehicle (UAV) individual (ID) identification is a critical security surveillance strategy in low-altitude integrated sensing and communication (ISAC) networks.<n>We propose a novel dynamic knowledge distillation (KD)-enabled wireless radio frequency fingerprint large language model (RFF-LLM) framework for UAV ID identification.<n>Experiment results show that the proposed framework achieves 98.38% ID identification accuracy with merely 0.15 million parameters and 2.74 ms response time.
  arXiv  Detail & Related papers  (2025-08-18T03:14:44Z)
• Efficient Real-Time Aircraft ETA Prediction via Feature Tokenization Transformer [17.277512318874457]
  Estimated time of arrival (ETA) for airborne aircraft in real-time is crucial for arrival management in aviation.<n>In this study, we utilize a feature tokenization-based Transformer model to efficiently predict aircraft ETA.<n>With a data sampling rate of 1HZ, the ETA prediction is updated every second.
  arXiv  Detail & Related papers  (2025-07-31T03:56:30Z)
• Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors [64.68349896377629]
  This work presents a novel autonomous end-to-end system that addresses the monocular relative localization, through deep neural networks (DNNs), of two peer nano-drones. To cope with the ultra-constrained nano-drone platform, we propose a vertically-integrated framework, including dataset augmentation, quantization, and system optimizations. Experimental results show that our DNN can precisely localize a 10cm-size target nano-drone by employing only low-resolution monochrome images, up to 2m distance.
  arXiv  Detail & Related papers  (2023-03-03T14:14:08Z)
• Towards a Fully Autonomous UAV Controller for Moving Platform Detection and Landing [2.7909470193274593]
  We present an autonomous UAV landing system for landing on a moving platform. The proposed system relies only on the camera sensor, and has been designed as lightweight as possible. The system was evaluated with an average deviation of 15cm from the center of the target, for 40 landing attempts.
  arXiv  Detail & Related papers  (2022-09-30T09:16:04Z)
• Edge YOLO: Real-Time Intelligent Object Detection System Based on Edge-Cloud Cooperation in Autonomous Vehicles [5.295478084029605]
  We propose an object detection (OD) system based on edge-cloud cooperation and reconstructive convolutional neural networks. This system can effectively avoid the excessive dependence on computing power and uneven distribution of cloud computing resources. We experimentally demonstrate the reliability and efficiency of Edge YOLO on COCO 2017 and KITTI data sets.
  arXiv  Detail & Related papers  (2022-05-30T09:16:35Z)
• Deep Learning for Real Time Satellite Pose Estimation on Low Power Edge TPU [58.720142291102135]
  In this paper we propose a pose estimation software exploiting neural network architectures. We show how low power machine learning accelerators could enable Artificial Intelligence exploitation in space.
  arXiv  Detail & Related papers  (2022-04-07T08:53:18Z)
• EAutoDet: Efficient Architecture Search for Object Detection [110.99532343155073]
  EAutoDet framework can discover practical backbone and FPN architectures for object detection in 1.4 GPU-days. We propose a kernel reusing technique by sharing the weights of candidate operations on one edge and consolidating them into one convolution. In particular, the discovered architectures surpass state-of-the-art object detection NAS methods and achieve 40.1 mAP with 120 FPS and 49.2 mAP with 41.3 FPS on COCO test-dev set.
  arXiv  Detail & Related papers  (2022-03-21T05:56:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.