Unmanned surface vessel (USV) systems for bridge inspection : final report.
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Summary
This report addresses the challenge of inspecting bridge infrastructure that spans waterways, a task currently hindered by environmental hazards such as strong currents, waves, and turbid water. With approximately 85% of U.S. highway bridges spanning water and many structures susceptible to corrosion in the "splash zone," the Florida Department of Transportation (FDOT) sought technological solutions to make inspections safer, more efficient, and less costly. The study evaluates the suitability of Unmanned Surface Vessels (USVs) for this purpose, comparing them against Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs). The authors conclude that USVs are the most viable option for comprehensive bridge inspection, capable of performing both surface and underwater surveys without the limitations of tethered or fully submerged systems. To validate this approach, researchers from Florida Atlantic University developed and tested a proof-of-concept system using a WAM-V USV16 platform. The vessel was outfitted with a real-time imaging sonar (Aris 1800) for underwater visualization and a digital camera for above-water inspection. The study involved a comprehensive review of current inspection practices, USV design considerations, and acoustic sensing technologies. Field experiments were conducted at multiple sites in Florida, including Carrabelle and Dania Beach, to evaluate the system’s ability to navigate, maintain position, and capture data. The experimental design focused on testing the integration of the sonar system with the USV’s guidance and control architecture, specifically examining station-keeping capabilities and path following along preprogrammed waypoints. The field tests demonstrated that the USV could autonomously collect images of bridge structures, including pilings and channel bottoms, by traversing waypoints and station-keeping at locations of interest. The system successfully captured acoustic images of underwater features and visual images of the waterline. However, the study identified several operational challenges, particularly regarding localization, precise station-keeping in dynamic environments, and the alignment of imaging sensors. The results indicated that while the current system could perform preliminary surveys, it required significant improvements in control algorithms and human-robot interaction to be fully effective for routine inspection tasks. The significance of this work lies in its provision of a validated framework for integrating USVs into FDOT’s standard operating procedures. The report offers specific recommendations for future research, emphasizing the need for advanced robotics techniques such as 3D obstacle avoidance, improved teleoperation interfaces, and cooperative sensing to handle large datasets. By establishing that USVs can effectively bridge the gap between manual diver inspections and fully autonomous systems, the study supports the development of semi-autonomous inspection teams. This approach promises to enhance the safety of inspection personnel while improving the consistency and coverage of bridge maintenance assessments.
Key finding
The USV system autonomously collected images of underwater and waterline bridge structures by traversing preprogrammed waypoints and station-keeping at locations of interest.
Methodology
field_study
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 24 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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