Flying Robotics Art: ROS-based Drone Draws the Record-Breaking Mural
- URL: http://arxiv.org/abs/2511.03651v1
- Date: Wed, 05 Nov 2025 17:09:16 GMT
- Title: Flying Robotics Art: ROS-based Drone Draws the Record-Breaking Mural
- Authors: Andrei A. Korigodskii, Oleg D. Kalachev, Artem E. Vasiunik, Matvei V. Urvantsev, Georgii E. Bondar,
- Abstract summary: This paper introduces a robust system capable of navigating and painting outdoors with unprecedented accuracy.<n>Key to our approach is a novel navigation system that combines an infrared (IR) motion capture camera and LiDAR technology.<n>We employ a unique control architecture that uses different regulation in tangential and normal directions.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: This paper presents the innovative design and successful deployment of a pioneering autonomous unmanned aerial system developed for executing the world's largest mural painted by a drone. Addressing the dual challenges of maintaining artistic precision and operational reliability under adverse outdoor conditions such as wind and direct sunlight, our work introduces a robust system capable of navigating and painting outdoors with unprecedented accuracy. Key to our approach is a novel navigation system that combines an infrared (IR) motion capture camera and LiDAR technology, enabling precise location tracking tailored specifically for largescale artistic applications. We employ a unique control architecture that uses different regulation in tangential and normal directions relative to the planned path, enabling precise trajectory tracking and stable line rendering. We also present algorithms for trajectory planning and path optimization, allowing for complex curve drawing and area filling. The system includes a custom-designed paint spraying mechanism, specifically engineered to function effectively amidst the turbulent airflow generated by the drone's propellers, which also protects the drone's critical components from paint-related damage, ensuring longevity and consistent performance. Experimental results demonstrate the system's robustness and precision in varied conditions, showcasing its potential for autonomous large-scale art creation and expanding the functional applications of robotics in creative fields.
Related papers
- Precision Meets Art: Autonomous Multi-UAV System for Large Scale Mural Drawing [0.0]
This paper presents the design, deployment, and testing of a novel multi-drone system for automated mural painting in outdoor settings.<n>New software that coordinates multiple drones simultaneously, utilizing state-machine algorithms for task execution.<n>A 100 square meters mural was created using the developed multi-drone system, validating the system's efficacy.
arXiv Detail & Related papers (2026-01-10T10:00:23Z) - SpectraSentinel: LightWeight Dual-Stream Real-Time Drone Detection, Tracking and Payload Identification [0.0903415485511869]
The proliferation of drones in civilian airspace has raised urgent security concerns.<n>In response to the 2025 VIP Cup challenge tasks, we propose a dual-stream drone monitoring framework.<n>Our approach deploys independent You Only Look Once v11-nano (YOLOv11n) object detectors on parallel infrared (thermal) and visible (RGB) data streams.
arXiv Detail & Related papers (2025-07-30T13:10:13Z) - NOVA: Navigation via Object-Centric Visual Autonomy for High-Speed Target Tracking in Unstructured GPS-Denied Environments [56.35569661650558]
We introduce NOVA, a fully onboard, object-centric framework that enables robust target tracking and collision-aware navigation.<n>Rather than constructing a global map, NOVA formulates perception, estimation, and control entirely in the target's reference frame.<n>We validate NOVA across challenging real-world scenarios, including urban mazes, forest trails, and repeated transitions through buildings with intermittent GPS loss.
arXiv Detail & Related papers (2025-06-23T14:28:30Z) - AI-Enhanced Automatic Design of Efficient Underwater Gliders [60.45821679800442]
Building an automated design framework is challenging due to the complexities of representing glider shapes and the high computational costs associated with modeling complex solid-fluid interactions.<n>We introduce an AI-enhanced automated computational framework designed to overcome these limitations by enabling the creation of underwater robots with non-trivial hull shapes.<n>Our approach involves an algorithm that co-optimizes both shape and control signals, utilizing a reduced-order geometry representation and a differentiable neural-network-based fluid surrogate model.
arXiv Detail & Related papers (2025-04-30T23:55:44Z) - Open-World Drone Active Tracking with Goal-Centered Rewards [62.21394499788672]
Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations.<n>We propose DAT, the first open-world drone active air-to-ground tracking benchmark.<n>We also propose GC-VAT, which aims to improve the performance of drone tracking targets in complex scenarios.
arXiv Detail & Related papers (2024-12-01T09:37:46Z) - Enhancing Autonomous Navigation by Imaging Hidden Objects using Single-Photon LiDAR [12.183773707869069]
We present a novel approach that leverages Non-Line-of-Sight (NLOS) sensing using single-photon LiDAR to improve visibility and enhance autonomous navigation.<n>Our method enables mobile robots to "see around corners" by utilizing multi-bounce light information.
arXiv Detail & Related papers (2024-10-04T16:03:13Z) - A Safer Vision-based Autonomous Planning System for Quadrotor UAVs with
Dynamic Obstacle Trajectory Prediction and Its Application with LLMs [6.747468447244154]
This paper proposes a vision-based planning system that combines tracking and trajectory prediction of dynamic obstacles to achieve efficient and reliable autonomous flight.
We conduct experiments in both simulation and real-world environments, and the results indicate that our approach can successfully detect and avoid obstacles in dynamic environments in real-time.
arXiv Detail & Related papers (2023-11-21T08:09:00Z) - Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone
Racing [52.50284630866713]
Existing systems often require hand-engineered components for state estimation, planning, and control.
This paper tackles the vision-based autonomous-drone-racing problem by learning deep sensorimotor policies.
arXiv Detail & Related papers (2022-10-26T19:03:17Z) - VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait
Representation [78.92147339883137]
We show that it is pivotal in increasing controller robustness by learning a latent space capturing the key stance phases constituting a particular gait.
We demonstrate that specific properties of the drive signal map directly to gait parameters such as cadence, footstep height and full stance duration.
The use of a generative model facilitates the detection and mitigation of disturbances to provide a versatile and robust planning framework.
arXiv Detail & Related papers (2022-05-02T19:49:53Z) - Autonomous Aerial Robot for High-Speed Search and Intercept Applications [86.72321289033562]
A fully-autonomous aerial robot for high-speed object grasping has been proposed.
As an additional sub-task, our system is able to autonomously pierce balloons located in poles close to the surface.
Our approach has been validated in a challenging international competition and has shown outstanding results.
arXiv Detail & Related papers (2021-12-10T11:49:51Z) - Machine Learning-Based Automated Design Space Exploration for Autonomous
Aerial Robots [55.056709056795206]
Building domain-specific architectures for autonomous aerial robots is challenging due to a lack of systematic methodology for designing onboard compute.
We introduce a novel performance model called the F-1 roofline to help architects understand how to build a balanced computing system.
To navigate the cyber-physical design space automatically, we subsequently introduce AutoPilot.
arXiv Detail & Related papers (2021-02-05T03:50:54Z)
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.