Innovation From Concept to Commercialization

construction industry robotics

Robotic Roadworks & Excavation System — Revolutionizing Utility Excavation Methods

ULC Robotics, Inc. and SGN are developing an advanced, all-electric robotic system to revolutionize the way that roadworks are performed. The Robotic Roadworks and Excavation System (RRES) will replace conventional methods of excavation performed daily by gas networks on transmission and distribution mains and other underground infrastructure.

Key Benefits

01. Improve Efficiency

RRES aims to expand the use of innovative core and vac methods to improve the efficiency of the operation, minimize time in the street and reduce costs.

02. Lower Risk of Damage

Using innovating soft-touch excavation technology and methods, artificial intelligence and machine learning, RRES aims to reduce accidental damage to buried utilities.

03. Higher Repeatability

Automating methods of carrying out works on the distribution and transmission system ensure routine works are completed uniformly with high precision.

04. Enhance Safety

RRES will limit the need for utility workers to enter excavation to improve worker safety. Minimized risk of accidental damages also improves public safety.

05. Minimize Disruption

RRES project sites will take up less space in the street to minimize disruption to communities and local businesses.

06. Reduce CO2 Emissions

By reducing the need for heavy construction vehicles on sites, the RRES project will enable gas networks to reduce carbon emissions.

Project Overview

Traditional excavation methods in the utility industry face a wide range of challenges such as third party damage, disruption to traffic, road closures and excessive carbon emissions. While our world has been rapidly transformed by technology, roadworks are still carried out using backhoes and dump trucks. ULC Robotics and SGN are developing an advanced robotic system to revolutionize the way utility companies, energy networks and other industries carry out roadworks. The Robotic Roadworks and Excavation System project is combining below-ground locating sensors, artificial intelligence, machine vision and new excavation methods for safer, faster and smarter roadworks.

End-to-End Process

01. Below Ground Sensing

Before beginning the actual excavation process, the RRES will deploy integrated below-ground sensors to ensure the robot is digging in the correct location and attempt to identify any other buried utilities that may impede the work.

02. Coring and Cutting Roadways

To gain access to the pipeline network under the street, the robotic system will cut a core out of the road surface. The core will then be picked up and set aside for reinstatement at the end of the project.

03. Automated Soft Touch Excavation

ULC Robotics is combining machine vision and sensors with newly developed soft-touch excavation methods to enable the robot to rapidly and autonomously remove the soil one layer at a time while identifying and avoiding other buried utilities.

04. Conduct Work on the Pipe

Once the pipeline has been exposed, the robot will be capable of installing a specialized fitting onto the pipe. Future development of the RRES will aim to expand the range of operations the robot can perform.

05. Backfill and Reinstatement

Lastly, the Robotic Roadworks and Excavation System will backfill the opening in the street and reinstate the core to provide a complete, end-to-end solution.

Below Ground Sensing

Prior to starting excavation, and during the excavation process, RRES uses sensors such as Ground Penetrating Radar and electromagnetic waves to scan the ground to identify buried assets in its excavation path. Advanced AI will be used to detect buried infrastructure and the target assets prior to cutting the road surface.

rres robotic gpr sensing

robotic roadworks and excavation system

Cutting the Road Surface

Our team has designed a custom concrete cutting chainsaw for use as an end effector for the robotic arm. This new and unique tool will enable RRES to make flexibly-shaped and sized cuts in a wide array of road surface materials.

Core Removal

Prior to cutting the road surface, RRES drills a small pilot hole in the road. Once the core is cut, this pilot hole is used to lift the core and set it aside. At the end of the project, the core is lifted and set back in place to reduce disturbance to the roadway and maintain their strength and integrity.

robotic excavator

Soft-Touch Excavation

RRES uses a compact, custom-designed vacuum excavation head with integrated super-sonic air nozzles to agitate and remove soil without the risk of damage to buried assets. Machine vision will be used during the excavation process to identify objects and guide activities through the excavated keyhole.

Backfill and Reinstatement

RRES will be equipped with additional end effectors to help backfill the excavation, compress soil and secure the core back in place to complete the end-to-end roadworks process.

construction robots

machine vision for excavation

Machine Learning and Machine Vision

In order to embed the RRES with the ability to ‘see’ its environment, the RRES team is developing 3D visualization techniques to capture 3D point clouds of the excavation and surrounding site.

All-Electric Construction Robot:

RRES is an all-electric robotic platform being developed with utility and construction industry applications in mind.

Project Partners

SGN manages the network that distributes natural and green gas to 5.9 million homes and businesses across Scotland and the south of England. Their dedicated and established innovation team manages ideas and opportunities, both from internal and external sources, co-ordinating their evaluation, and prioritising and converting into value propositions projects that align with their innovation strategy.

ULC Technologies is a leading robotics engineering company for the energy industry, transforming the way that vital infrastructure is maintained and operated. As the key RRES project partner, ULC’s team of mechanical and electrical engineers, sensor scientists, technicians, project managers and machine learning specialists are responsible for designing and developing the field demonstrable prototype of the RRES.

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