Autonomous Aerial Non-Destructive Testing: Ultrasound Inspection with a Commercial Quadrotor in an Unstructured Environment

Ruben Veenstra; Barbara Bazzana; Sander Smits; Antonio Franchi

cs.ROMar 4, 2026

Autonomous Aerial Non-Destructive Testing: Ultrasound Inspection with a Commercial Quadrotor in an Unstructured Environment

Authors: Ruben Veenstra, Barbara Bazzana, Sander Smits, Antonio Franchi

Organizations: Robotics and Mechatronics lab, Faculty of Electrical Engineering, Mathematics & Computer Science, University of Twente, Enschede, The Netherlands. · Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy.

Abstract

This work presents an integrated control and software architecture that enables arguably the first fully autonomous, contact-based non-destructive testing (NDT) using a commercial multirotor originally restricted to remotely-piloted operations. To allow autonomous operation with an off-the-shelf platform, we developed a real-time framework that interfaces directly with its onboard sensor suite. The architecture features a multi-rate control scheme: low-level control is executed at 200 Hz, force estimation at 100 Hz, while an admittance filter and trajectory planner operate at 50 Hz, ultimately supplying acceleration and yaw rate commands to the internal flight controller. We validate the system through physical experiments on a Flyability Elios 3 quadrotor equipped with an ultrasound payload. Relying exclusively on onboard sensing, the vehicle successfully performs autonomous NDT measurements within an unstructured, industrial-like environment. This work demonstrates the viability of retrofitting off-the-shelf platforms for autonomous physical interaction, paving the way for safe, contact-based inspection of hazardous and confined infrastructure.

Figures & tables

Fig. 1 : The overview of our system, allowing to get NDT data through compliant interaction with an inspection surface, using onboard sensors. The algorithm is tested on the Flyability Elios3 in the industrial testbed at the University of Twente, Enschede, The Netherlands.

Fig. 2 : Flyability Elios3 System with highlight of the onboard sensors and equipment. LiDAR, vision and distance sensors are used for localization, the UT payload and the couplant gel for NDT data collection, the 4k optical camera and the thermal camera are shown online to the operator to provide with a clear understanding of the scenario, when operating the drone remotely.

Fig. 3 : Inspection algorithm flow diagram.

Fig. 4 : The constitutive modules of the control architecture, with the exchanged signals. The low-level control is proprietary software of Flyability, which is enclosed but can reliably follow acceleration and yaw rate setpoints.

Module	Parameter	Value
Trajectory Generation	max_vel	$0.5\,m/s$
Trajectory Generation	max_acc	$0.25\,m/s^{2}$
Admittance Filter	$\mathbf{M}$	$0.5\,\mathbb{I}_{3\times 3}$
	$\mathbf{D}$	$7.5\,\mathbb{I}_{3\times 3}$
	$\mathbf{K}$	$\mathrm{diag}(30,30,100)$
Force Observer	$\mathbf{L}$	$7.5\,\mathbb{I}_{3\times 3}$

TABLE I : Modules and corresponding parameters

Panel of Figure 6 — Fig. 5 : (a) A wide view of the inspection location. An air duct is directly below the Elios3. The inspection takes place between rods, which may represent a collision risk. (b) The corresponding 3D map, as shown in the Cockpit App, together with the odometry.

Fig. 6 : Detaching maneuver to disengage the magnetic probe of the Elios3 UT payload.

Fig. 7 : Position, roll-pitch-yaw (

\phi,\theta,\psi

), and force observer plots of the Elios3 during the autonomous inspection flight. All quantities are represented in the global-map/world-frame. State of the inspection is indicated above the position plot numbers correspond to states from Sec. III-B . Green shading in the force plot indicate when measurement is good and stable as indicated by the Elios3 UT module.

Fig. 8 : Screenshot of the Cockpit App of the UT A-scan, showing a good and stable measurement.

Flight	RMSE [m]			Position error [m]
Flight	$e_{x}$	$e_{y}$	$e_{z}$	$e_{y}$	$e_{z}$
Flight 1	0.030	0.026	0.051	0.046	0.038
Flight 2	0.031	0.025	0.045	0.052	0.028
Flight 3	0.031	0.026	0.046	0.053	0.027

TABLE II : RMSE errors during inspection procedure of all the autonomous flights

Fig. 9 : Force observer during manual inspections. Green and yellow shading in the force plot indicate when measurement is good and stable and unstable respectively as indicated by the Elios3 UT payload.

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This paper presents a non-contact approach for vibration-based structural damage detection using an autonomous and customized cost-effective unmanned aerial vehicle (UAV). Vibration signals are extracted from video recordings through vision-based motion tracking to identify shifts in natural frequencies indicative of structural degradation. A laboratory-scale frame structure is evaluated under healthy and simulated-damage conditions. The proposed system is validated through an experimental study involving two smartphones, a USB camera, and a custom-built low-cost UAV equipped with an onboard camera and an autonomous alignment system for operation in GPS-denied environments. The displacement time is extracted and analyzed in the frequency domain and compared to reference measurements from contact accelerometers and a finite element model. Experimental results show that all platforms successfully capture the fundamental frequency and its shift due to damage. Although the UAV exhibits slightly higher errors (up to 5.7%) due to platform-induced disturbances and sensing limitations, it reliably detects damage-induced frequency changes. Compared to commercial UAV systems, the proposed platform achieves comparable inspection performance at significantly lower cost. These results demonstrate that low-cost autonomous UAVs provide a practical, flexible, and scalable solution for structural health monitoring, particularly in scenarios where contact-based sensing is impractical. The findings also support the potential for the deployment of multiple cooperative UAVs to further enhance inspection coverage and robustness.

Abstract

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