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.
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
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
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