Periodic Reporting for period 2 - TUBERS (Scalable and Modular robotic tools for pipeline inspection and repair)
Reporting period: 2023-09-01 to 2025-02-28
The water management industry is deeply affected by its construction inconsistencies. Specifically, new water pipes coexist with the older distribution system dating back 50 years. As a result, failures occur, and water leakages emerge. Yet, water leakage problems are challenging and difficult to contain or manage effectively.
Per year, there is a 20% average loss of treated water globally. So, assuming that leakages are left unattended, they can result in around 31.5 million m3/year of water loss and €16 million in revenue. At the same time, processing water and wastewater is energy-intensive and accounts for around 40% of municipal energy use, commonly produced by burning fossil fuels with carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emissions of up to 5%. Thus, a definitive solution should be found regarding problems with groundwater management, source water protection, and the treatment of existing and new wastewater.
TUBERS aims to build an ecosystem of robotic tools to holistically address the inspection and repair of pipelines within water networks and beyond by taking a radical approach and moving away from conventional solutions. This ecosystem will consist of a Snake-like Resident Robot, a High-accuracy inspection system, a Decision Support System (DSS) and Soft Repair Robots; TUBERS will perform day and night inspections over long distances & targeted in-situ repairs with a novel repair deployment mechanism to reduce inspection & maintenance costs.
Per year, there is a 20% average loss of treated water globally. So, assuming that leakages are left unattended, they can result in around 31.5 million m3/year of water loss and €16 million in revenue. At the same time, processing water and wastewater is energy-intensive and accounts for around 40% of municipal energy use, commonly produced by burning fossil fuels with carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emissions of up to 5%. Thus, a definitive solution should be found regarding problems with groundwater management, source water protection, and the treatment of existing and new wastewater.
TUBERS aims to build an ecosystem of robotic tools to holistically address the inspection and repair of pipelines within water networks and beyond by taking a radical approach and moving away from conventional solutions. This ecosystem will consist of a Snake-like Resident Robot, a High-accuracy inspection system, a Decision Support System (DSS) and Soft Repair Robots; TUBERS will perform day and night inspections over long distances & targeted in-situ repairs with a novel repair deployment mechanism to reduce inspection & maintenance costs.
Activities in this period focused on development of the prototype system modules and their validation in lab conditions using different scenarios. More specifically, the consortium:
-Further developed the snake-like robot’s autonomous navigation algorithm and improved its mechanical robustness.
-Developed a fully functional prototype of the soft robotic repair platform along with the necessary user controls (mechanical and software). Lab testing of robot navigation and repair functionalities were carried out.
-Electronics and software of the ultrasonic testing module have been completed and underwent extensive lab-testing. Mechanical design of the enclosure for integration with the snake-like robot has also been completed.
-Explainable machine learning algorithms for defect detection have been developed using real and synthetic data. A first version of the decision support system has been brought up, featuring inspection data visualisation, explainability features and maintenance planning analytics.
-Integration of the various components has begun and field trial plans have been drafted
-Further developed the snake-like robot’s autonomous navigation algorithm and improved its mechanical robustness.
-Developed a fully functional prototype of the soft robotic repair platform along with the necessary user controls (mechanical and software). Lab testing of robot navigation and repair functionalities were carried out.
-Electronics and software of the ultrasonic testing module have been completed and underwent extensive lab-testing. Mechanical design of the enclosure for integration with the snake-like robot has also been completed.
-Explainable machine learning algorithms for defect detection have been developed using real and synthetic data. A first version of the decision support system has been brought up, featuring inspection data visualisation, explainability features and maintenance planning analytics.
-Integration of the various components has begun and field trial plans have been drafted
The following objectives have been defined as necessary in the path for commercialisation of the TUBERS system:
• Upgrade the snake-like resident robot and implement the necessary navigation software for autonomous operation over long distances and effective negotiation of pipeline features such as junctions and bends.
• Develop a modular soft-robotic platform with a locomotion mechanism and a novel mechanism capable of inspecting and repairing pipe segments from the inside.
• Research and develop a high-accuracy ultrasonic inspection system and the respective interpretation modules that can detect leaks and, most importantly, measure wall thickness loss.
• Research and develop a Decision Support System powered by Explainable Machine Learning algorithms for holistic inspection and maintenance planning.
• Upgrade the snake-like resident robot and implement the necessary navigation software for autonomous operation over long distances and effective negotiation of pipeline features such as junctions and bends.
• Develop a modular soft-robotic platform with a locomotion mechanism and a novel mechanism capable of inspecting and repairing pipe segments from the inside.
• Research and develop a high-accuracy ultrasonic inspection system and the respective interpretation modules that can detect leaks and, most importantly, measure wall thickness loss.
• Research and develop a Decision Support System powered by Explainable Machine Learning algorithms for holistic inspection and maintenance planning.