使用 Dronar 对水渠进行非侵入式检测
Non-Invasive Inspection of Water Canals Using Dronar
- Northern Arizona University（北亚利桑那大学）
- Rochester Institute of Technology（罗切斯特理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对开放式混凝土水渠检测难的问题，提出集成无人机与声纳的 dronar 系统，现场测试验证其能非侵入式检测渠床沉积，且测量可重复。
AI中文摘要：
开放式混凝土水渠在输水中发挥着至关重要的作用，是亚利桑那州凤凰城都市区数百万人的主要水利基础设施。随着时间的推移，混凝土水渠可能会出现一系列问题，包括渠衬变形、混凝土开裂以及渠底沉积物堆积。识别此类关键问题是一个资源密集型过程，目前仅在四年一次的干涸周期内进行。这导致维护人员无法优先处理受影响最严重的水渠段。为解决这一问题，研究团队开发并验证了一种易于部署且非侵入式的方法，可在不排干水的情况下检测渠床。该检测系统集成了价格实惠的现成无人机和声纳技术（称为 dronar）。该 dronar 系统包括一个消费级声纳系统，集成在无人水面艇（USV）上，该艇将声纳换能器恰好置于渠水表面之下。本文展示了 dronar 系统在凤凰城亚利桑那水渠上三次现场测试中的概念验证演示。沉积渠段的 DownScan 深度剖面始终比同一渠段清理后的剖面更浅，沿轨迹观测到的偏移量高达 15 厘米。对干净渠段的重复运行产生了紧密重叠的 DownScan 深度剖面，证实 dronar 在渠床自然变化中能产生可重复的测量结果。这些结果确立了 dronar 作为非侵入式渠床检测可行概念验证工具的地位。
英文摘要：
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.
