Total Project Value:
€ 2 700 000,00
from 01/12/2020 to 31/05/2023
S2R (Of H2020) co-funding:
€ 2 700 000,00
Christian Di Natali
FONDAZIONE ISTITUTO ITALIANO DI TECNOLOGIA
Although robot technologies have a strong foothold in a variety of industrial fields, robotics applied to safe railway construction, inspection, and maintenance has lagged behind. Currently, throughout Europe, railway work is carried out by workers and human-operated on-track machines (OTMs), with the quality of the work being closely related to the compliance, diligence, experience and competence of individual workers. Moreover, some of the tools used by the workers are heavy, requiring significant physical effort, which over time can have a detrimental effect on the human body. On top of these, both manual railway workers and machine operators are constantly subject to a varying degree of cognitive burden, which undermines the quality, repeatability, and productivity of their work. This can potentially lead to a significant risk of accidents caused by human errors. Railway worksites have not yet taken advantage of the inherent benefits of robotic technologies, which could lead to increasing quality (repeatability and accuracy), productivity and improving worker safety. The necessary quality and operation time, typical for maintenance and renewal works, is hard to standardize across European companies and shared maintenance approaches without the use of smart technologies. Furthermore, the SoA practices are not compatible with the LEAN execution of an intelligent maintenance process, due to heterogeneous working methods arising from the use of special-purpose OTMs in the rail industry. These shortcomings, however, may be addressed through the adoption of already tried and tested technologies and robotic solutions proven in other industrial fields (e.g. Industry 4.0).
Giving the existing OTMs a level of autonomy and intelligence through robotic principles can avoid many incidents and accidents at the worksite, e.g. prevention of collisions between machine and other machine, infrastructure or - worst of all - with workers. Introducing autonomy could make safe collaboration between machine operations and workers in a shared workspace possible. This form of physical Human-Robot Interaction (HRI) is now common with robots, and it could form an important real-world objective when aiming towards LEAN maintenance in the rail industry. Therefore, digitalized maintenance process integration and physical Human-Machine Interaction (HMI) should be considered when designing new, more collaborative tools and working methods. This could lead to significant reductions in costs and time spent on railway infrastructure and equipment projects.
In addition to the before mentioned productivity challenges, societal attitudes and trends are an important consideration. Historically, the railway industry is one of the main employers in Europe, with over 1 million people employed in the sector at the end of 2016. This report highlights that the ageing of the workforce continues to be a concern, especially in Spain, Greece, and Italy, where over 50% of the workforce were over 50 years old in 2016. At the same time, these physically demanding occupations are unattractive to the European youth. In addition, the physical demands of the jobs mean that Musculo-Skeletal Disorder (MSD) affects up to 38.1% of workers during their careers. This is a cross-border phenomenon, which concerns all industrial fields and EU countries.
This triplet of a rapidly ageing workforce, a low interest in physically demanding work among the young, and the toll imposed by physically demanding occupations poses a great challenge for the future of the rail industry. By “robotizing” the OTMs and tools, and by introducing aid devices for supporting workers during demanding activities, the main objective is to achieve an increase in the attractiveness and competitiveness of the European railway sector by creating an efficient, safe, intelligent infrastructure inspection and maintenance approach.
STREAM main objective is to develop innovative technologies to improve rail inspection and maintenance operations in a way that creates benefits in quality of operations, and workers health, safety, and dignity.
This is achieved by (i) enhancing current OTMs to create multi-purpose autonomous devices and systems, that increase the level of task execution safety, quality and productivity by applying robotic principles; and (ii) deploying wearable assistive exoskeletons, that use advanced proprioceptive solutions to supply on-demand mechanical power, reducing the risk of injury and related costs.
More specifically, the main goal of WS1 is the development of a generic design framework for an On-Track Autonomous Multi-purpose Mobile Manipulator (OTA3M). This truly multi-purpose OTA3M will be capable of replacing a large set of current railway operations carried out by human workers and/or special purpose OTMs in construction, inspection, maintenance, and renewal. The OTA3M will be capable of manipulating heavy rail infrastructure components and materials, ranging from a few kilos to several tons, with an arm reach of several meters. Our innovative idea is to develop control methods and toolboxes to convert any-brand of OTM (i.e. hydraulic Road-Rail Excavator (RRE)) into an autonomous on-track mobile robot with the on-board 6 Degree Of Freedom (DOF) robotic excavator arm. We aim to enhance worksite safety by introducing automatic safety functions to protect workers and thus allow safe and efficient human-machine collaboration based on the established principles of physical HRI. At the same time, this will decrease the cognitive burden on machine operators and consequently reduce the occurrence of fatal accidents due to human errors. We aim to reduce task operation time by at least 20%, by improving effectiveness and accuracy, while providing robot-like high-performance task repeatability.
In parallel, the main goal of WS2 is to develop a Modular Multitasking Powered Exoskeleton (MMPE). The exoskeleton will reduce musculoskeletal loading by up to 50% during manual handling tasks (e.g. holding, lifting, pulling, pushing, and carrying). The device will be usable in unstructured working environments and with unstructured methodologies. The MMPE will be able to assist in multiple different tasks and operations by detecting the user activity in real-time and helping workers reduce the experienced load. The main objective is to develop a robotic system that can be worn throughout the work shift with minimal/zero discomfort or hinder, while adapting its level and form of aid to the user defined needs.