Non-Invasive Inspection of Water Canals Using Dronar
Authors: Michael Zielinski, Zhizhan Wang, Benjamin Dymond, Reza Razavian, Zhongwang Dou
Organizations: Mechanical Engineering Department, Steve Sanghi College of Engineering, Northern Arizona University, Flagstaff, AZ 86011, USA. · Civil and Environmental Engineering Department, Steve Sanghi College of Engineering, Northern Arizona University, Flagstaff, AZ 86011, USA · Department of Biomedical Engineering, Kate Gleason College of Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA
Open concrete canals play a vital role in water transportation, serving as primary water infrastructure for millions of people across the Phoenix, Arizona, metro area. Over time, the concrete canals can experience a range of issues, including canal lining deformation, cracked concrete, and sediment buildup on the canal floor. Identifying such critical issues is a resource-intensive process, which currently happens only during four-year dry-up cycles. This prevents the maintenance crew from prioritizing operations on the most affected canal segments. To address this issue, the research team has developed and verified an easily deployable and non-invasive method to inspect canal beds without draining the water. This inspection system integrates affordable, off-the-shelf drone and sonar technology (termed dronar). This dronar system includes a consumer-grade sonar system integrated into an unmanned surface vehicle (USV) that carries the sonar transducer just under the surface of the canal water. This paper presents a proof-of-concept demonstration of the dronar system across three field tests on the Arizona Canal in Phoenix. DownScan depth profiles from the sedimented canal segment were consistently shallower than profiles from the same segment after cleaning, with offsets of up to 15 cm observed along the track. Repeated runs over the clean segment produced closely overlapping DownScan depth profiles, confirming that the dronar yields repeatable measurements across the natural variation of the canal bed. These results establish the dronar as a viable proof-of-concept tool for non-invasive canal bed inspection.
Figures & tables
Figure 1 : Example concrete water canal in the SRP system.
Figure 2 : Details of the custom-built unmanned surface vehicle and sonar, together referred to as dronar.
Figure 3 : ( a ) Dronar transport and deployment cart; ( b ) dronar system on deployment cart before launch; ( c ) CAD model of the USV being lowered on the cart; ( d ) CAD model of the USV being released from cart.
Figure 4 : Dronar proof-of-concept test location between N 56th St and N Arcadia Dr in Phoenix, AZ.
Figure 5 : Dronar pre- and post-maintenance test location at North Mesa Drive in Mesa, AZ.
Figure 6 : Dronar USV traveling upstream in Manual mode.
Figure 7 : ( a ) USV floating next to the cart prior to recovery; ( b ) USV recovery, with the left motor sitting on the rails but the cart hook still grabbing the frame and holding the USV secure.
Figure 8 : USV being recovered utilizing the gaff pole.
Figure 9 : SideScan (top) and DownScan (bottom) sonar imagery from the clean canal segment in Test 2b post-maintenance (Feb. 24, 2026). Both images are along the canal length; SideScan is a top-down view while DownScan is as if the user is looking from the side of the canal. Only DownScan data were used for the depth profile analyses in this study.
Figure 10 : DownScan depth profiles (m) for the two canal water current speed runs during Test 2b: post-maintenance (Feb. 24, 2026), showing raw and band-pass-filtered data.
Figure 11 : DownScan depth profiles (m) along the Test 2 segment, shown as raw and bandpass filtered. Distance along canal path (m) is measured downstream from the Mesa Drive canal crossing; the x-axis is cropped to a representative subsection to show the contrast between sedimented and clean canal bed conditions.
A Remotely Operated Vehicle (ROV) is a tethered underwater robot used for tasks like inspection and intervention. While essential tools for underwater science, the high cost of commercial ROVs and a persistent gap between mechanically capable platforms and those with integrated intelligence create a significant barrier to access. HyDRA Scorpion differs from conventional systems by addressing these challenges, integrating an advanced, AI-driven perception stack with in-situ measurement capabilities onto a low-cost, locally manufacturable platform. The system combines 4-DoF maneuverability, dual manipulators, and a custom pressure-tested housing. Experimental results validate the system's robustness and performance. Leak-free operation was confirmed through prolonged pressure testing of the electronics housing to 4 bar, equivalent to the pressure of a 304.8-meter water depth approximately in a simulated environment, with no moisture ingress detected. The vehicle also demonstrated stable station-keeping, maintaining its position within a tight tolerance of $$\pm0.15 meters under external disturbances. The onboard AI module achieved underwater object detection mean Average Precision (mAP) of 0.89 with real-time inference, length and 3D-mapping based distance measurement. Also, 4-DoF manipulator arm can grip and maintain dual-function manipulator feature which support 360 degree tangle-free rotation.
Department of Computer Science and Engineering, United International University, United City, Madani Ave, Dhaka 1212, Bangladesh · Department of Electrical and Electronic Engineering, United International University, United City, Madani Ave, Dhaka 1212, Bangladesh
Most existing drone-based inspection systems require the drone to fly dangerously close to the target or follow complex flight paths to capture small details. In addition, drone flight is affected by disturbances and localization inaccuracies, which can cause the drone to lose sight of its supposed target when it has a narrow view. Furthermore, trajectory planning often requires prior information about the target's geometry, position, and orientation, which is not always available for non-structural targets such as trees, vehicles, or people. To address these challenges, this paper presents aerial_micro_inspection, a generic pipeline for aerial micro-inspection across different use cases. The pipeline assumes a PX4-powered drone equipped with two cameras: (i) a zoomed, gimbal-mounted inspection camera that captures fine details without requiring the drone to fly very close to the target, and (ii) a wide-field-of-view stereo navigation camera that acquires the target surface on site, estimates its range, and partitions it into smaller inspection regions. In addition, a vision-based feedback loop compensates for drone motion while the inspection camera visits small partitions of a larger surface. We evaluate the pipeline in simulation and real-world experiments, mainly in two use-case scenarios: tree inspection for detecting oak processionary caterpillars and their eggs, and greenhouse inspection of sticky traps for detecting whiteflies. The results show improved coverage robustness under drone disturbances in simulation, as well as effective detection of caterpillars and eggs and high-detail imaging of insects in real-world experiments. The pipeline is open-source, developed in ROS 2, and can be adapted to new applications by replacing the surface-segmentation and micro-target detection checkpoints. The code is available at: https://github.com/SaxionMechatronics/aerial_micro_inspection
S. H. Mirtajadini, N. Rublein, R. M. Ramakrishnan +3
Smart Mechatronics and Robotics (SMART) Research Group, Saxion University of Applied Sciences, Enschede, The Netherlands · Faculty of Science and Engineering, University of Groningen, The Netherlands
Reliable pipeline inspection is critical to safe energy transportation, but is constrained by long distances, complex terrain, and risks to human inspectors. Unmanned aerial vehicles provide a flexible sensing platform, yet reliable autonomous inspection remains challenging. This paper presents an autonomous quadrotor near-proximity pipeline inspection framework for three-dimensional scenarios based on image-based visual servoing model predictive control (VMPC). A unified predictive model couples quadrotor dynamics with image feature kinematics, enabling direct image-space prediction within the control loop. To address low-rate visual updates, measurement noise, and environmental uncertainties, an extended-state Kalman filtering scheme with image feature prediction (ESKF-PRE) is developed, and the estimated lumped disturbances are incorporated into the VMPC prediction model, yielding the ESKF-PRE-VMPC framework. A terrain-adaptive velocity design is introduced to maintain the desired cruising speed while generating vertical velocity references over unknown terrain slopes without prior terrain information. The framework is validated in high-fidelity Gazebo simulations and real-world experiments. In real-world tests, the proposed method reduces RMSE by 52.63% and 75.04% in pipeline orientation and lateral deviation in the image, respectively, for straight-pipeline inspection without wind, and successfully completes both wind-disturbance and bend-pipeline tasks where baseline method fails. An open-source nano quadrotor is modified for indoor experimentation.
Wen Li, Hui Wang, Jinya Su +3
School of Automation, Key Laboratory of Measurement and Control of CSE, Ministry of Education, Southeast University, Nanjing 210096, China · Department of Aeronautical and Automotive Engineering, Loughborough University, LE11 3TU, United Kingdom · Research Centre for Low Altitude Economy and the Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China