Technology Report 2022 - ULC Technologies

Improving Underground Surveying and Locating Using a Robotic Vehicle

Synopsis

The prototype Robotic Underground Survey System (RUSS) is a semi-autonomous system that assists crews in locating and surveying underground infrastructure to build accurate maps of buried pipelines and cables. The robot employs conventional GPR but uses an innovative approach to improve the sensitivity to both metallic and non-metallic pipes. It uses a non-conventional scanning method (known as multi-static data collection or the separation of transmitter and receiver antennas during scanning) and algorithms that improve the detection of low-reflectivity targets.

Testing showed the following advantages of the robotic system:

Increased resolution of scans
Improved detection capabilities for both metallic and non-metallic pipe
Semi-autonomous scanning that can improve reliability and consistency of scans
Locating accuracy that can meet most pipeline operator requirements
Locating buried infrastructure of all kinds can be challenging, and the proliferation of plastic pipe installations can make accurate locating even more of a challenge.

ULC is seeking additional partners to identify use cases for this solution, pilot the use of the technology in the field, and support commercialization.

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Snapshot

Company Name(s): ULC Technologies, LL
Company Website: https://ulctechnologies.com/
Technology Category: Locating, Mapping, Positioning
Stakeholder Target Audience: Designer, Facility Owner
Key terms /phrases: GPR, GPR technician, low-reflectivity targets, plastic pipe detection, robotic vehicle
Level of Production:
[technology being discussed at a level to determine if the manufacture should continue]

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Description of Solution Testing at Mock Roadway Testing Results Conclusion & Future Work

Introduction

For many years, Ground Penetrating Radar (GPR) has been used by utility locators to detect underground pipes. With the increasing amount of buried plastic pipe going into congested subspaces, locating is becoming more challenging since plastic offers lower reflectivity compared to metal. Third-party damage continues to be an ongoing problem nationwide. ULC Technologies received co-funding from the Pipeline and Hazardous Materials Safety Administration (PHMSA) to develop a robotic solution to improve the detection of plastic pipes. At the onset of the development, ULC spoke to several natural gas pipeline operators who conveyed that in addition to detection, the locating accuracy, timeliness of reporting, and the operating interface were key features that a locating or surveying system should feature. They also pointed out that since GPR technicians are not easy to find, electromagnetic locating is sometimes preferred over GPR even though GPR may provide better performance. Also, the lack of skilled technicians can cause significant delays in generating reports when using GPR.

Description of Solution

The prototype Robotic Underground Survey System (RUSS) shown in Figure 1 is a semi-autonomous system that assists crews in locating and surveying underground infrastructure to build accurate maps of buried pipelines and cables. The robot employs conventional GPR but uses an innovative approach to improve the sensitivity to both metallic and non-metallic pipes. The most unique attribute is the use of non-conventional scanning methods (known as multi-static data collection or the separation of transmitter and receiver antennas during scanning) and algorithms that improve the detection of low-reflectivity targets. The other important operating feature is the use of a fine grid for scanning which enables the use of 3D migration algorithms for improved reconstruction of the image. The ability to rotate the antenna and position the antenna in different locations enables novel ways of viewing underground targets. Robotic localization and navigation using multiple sensors provide increased locating accuracy to ensure that excavation can be performed at the right location. Scan data presented to an operator in the form of C-Scans and point clouds provide an increased understanding of objects underground due to higher image resolution.

Figure 1: Robotic Underground Survey System (RUSS)

Robot features include:

Battery Powered
Semi-autonomous and wireless control by an operator
Onboard sensors include LIDAR, GNSS receiver, ultrasonic sensors, IMU, and wheel encoders
Collision avoidance when performing automated scans for safe operation
Curb climbing over most curbs (up to 12” height)
Operation in urban, suburban, and rural areas (24” tires)

By using advanced algorithms for image reconstruction, the C-scan and point cloud images are of higher resolution (denser) and easier to interpret.

Reduces the false positives and near misses
Pipes at angles to the scan direction are much easier to detect compared to traditional scanning methods.

The robot provides improved performance and consistency compared to conventional manual scanning that employs a human operator pushing a single antenna mounted in a cart.

Testing at ULC's Mock Roadway

ULC Technologies designed and constructed mock roadways to represent a congested subsurface; the following were used for testing the robot:

Mock Roadway #1 has steel pipe with diameters ranging from 2” to 12” and HDPE pipes with diameters from 2” to 8”. Electrical wires in plastic conduits are also buried. The depth of lines varies from 1 to 4 ft. The soil is clayey with a 9” concrete surface.
Mock Roadway #2 has mostly plastic pipe with diameters ranging from 1” to 4”. Steel pipes and a concrete duct bank with electric cables are also buried. Pipes are buried in sandy soil at depths varying between 1.5’ and 5’.

Two scanning modes were employed. Figure 2 shows the most common scanning mode employed called common offset mode (Left) and a new, multi-static scanning approach (Right). In the former mode, both antennas are always moved together. In the latter mode, the receiver (blue) moves along the full scan length for each static, incremental position of the transmitter (yellow).

Figure 2: Scanning Modes Employed for Testing (Left) Common Offset Mode (Right) Multi-Static Data Mode

Figure 3: Robot deploying sensors using Common Offset Mode

Figure 4: Robot deploying sensors using Multi-Static Data Mode

Examples of C-scan data from testing are shown in Figure 5 and Figure 6. The images show various pipes that were detected within a 10 ft x 2 ft area at different depths. The images are of much higher resolution when compared to manual scanning and can make the interpretation of GPR much easier for an operator. At site #2, angled pipe (pipes at angles to the scan path) was successfully detected. This would have been more challenging if performed manually since reflections from the angled pipe are weaker.

Figure 5: Example C-Scan Images Showing Pipes at Different Burial Depths at Site #1

Figure 6: Example C-Scan Images Showing Pipes at Different Burial Depths at Site #2

Sizes of non-metallic pipes that were detected include:

8in HDPE @ 3ft depth
2in HDPE @ 1.5ft depth
3in HDPE conduit with copper wire @ 2.5ft depth
3in HDPE @ 1ft, 1.5ft, 2ft & 3ft depth

Greater depths can be achieved by using lower frequency antennas.

An example of a point cloud generated from the software is shown in Figure 7. High-resolution pictures such as this can also be presented to an operator to improve the understanding of the underground utilities.

The overall above-ground accuracy of locating the robot after sensor fusion in the world frame is observed to be under 16”. Operators are seeking a total accuracy of less than 24” while some are seeking better accuracy typically around 18”. Additional testing and algorithm tuning can improve locating accuracy even further.

Figure 7: 3D Point Cloud of the Underground Utilities

Testing Results

Testing showed the following advantages of the robotic system:

Increased resolution of scans
Improved detection capabilities for both metallic and non-metallic pipe
Semi-autonomous scanning that can improve reliability and consistency of scans
Locating accuracy that can meet most pipeline operator requirements

Conclusion & Future Work

ULC Technologies’ autonomous robotic scanning system demonstrated advantages over conventional scanned GPR and addresses common shortfalls of current GPR technology, operation, and reporting. In the future, this technology will reduce third-party damage by increasing the true positive detection rate and by reducing false alarms. Operator training requirements will be simplified, physical equipment handling requirements will be eased, and surveying consistency will be guaranteed. The overall time to detect, characterize, and classify the target asset will be reduced. Automatic generation of maps with accurate GPS data can be integrated into a utility company’s Geographical Information System (GIS) allowing utility companies to maintain up-to-date records.

ULC is seeking additional partners to identify use cases for this solution, pilot the use of the technology in the field and support commercialization.

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