Periodic Reporting for period 2 - MORE (Educating Europe`s Future Engineers in Next Generation Heavy Duty Mobile Machinery: Artificial Intelligence driven Robotisation, Energy Efficiency and Process Optimisation)
Reporting period: 2022-01-01 to 2024-06-30
Ever-increasing demand for material and food, stricter emissions regulations, and continuous globalisation has put Europe’s construction, logistics, and agriculture sectors under immense pressure. Dramatic improvements in their productivity and efficiency are the only answer to these global challenges. Heavy-duty mobile (HDM) machinery are crucial workhorses in these sectors.
MORE addressed these challenges by delivering innovative solutions driven by digitalisation and Artificial Intelligence (AI) in three areas:
1) Processes: To analyse and optimise processes involved in the use of HDM machinery, in particular, earth moving; To identify business cases for robotisation and energy efficiency related technologies.
2) To design and implement innovative energy-efficient concepts and solutions for mobile machine powertrains.
3) To devise transferable robotics control strategies for safe and energy-efficient operation of the future mobile machinery.
Europe’s HDM machine industry needs to invest heavily in digitalization, ‘green’ and innovative technologies, and address the serious labour shortage. There is thus a rapidly growing demand for experts in HDM machinery having the big picture of trends in AI, robotics, and energy systems as well as an understanding of the challenges and opportunities.
MORE is an innovative European Industrial Doctorate (EID) research and training programme, the first of its kind, that will address this need and fill the gap in related research and training. Eight early-stage researchers have been trained with a set of research skills including robotics, machine learning, energy systems, as well as transferable skills such as entrepreneurship and career management.
MORE addressed these challenges by delivering innovative solutions driven by digitalisation and Artificial Intelligence (AI) in three areas:
1) Processes: To analyse and optimise processes involved in the use of HDM machinery, in particular, earth moving; To identify business cases for robotisation and energy efficiency related technologies.
2) To design and implement innovative energy-efficient concepts and solutions for mobile machine powertrains.
3) To devise transferable robotics control strategies for safe and energy-efficient operation of the future mobile machinery.
Europe’s HDM machine industry needs to invest heavily in digitalization, ‘green’ and innovative technologies, and address the serious labour shortage. There is thus a rapidly growing demand for experts in HDM machinery having the big picture of trends in AI, robotics, and energy systems as well as an understanding of the challenges and opportunities.
MORE is an innovative European Industrial Doctorate (EID) research and training programme, the first of its kind, that will address this need and fill the gap in related research and training. Eight early-stage researchers have been trained with a set of research skills including robotics, machine learning, energy systems, as well as transferable skills such as entrepreneurship and career management.
Overview of the results: During the reporting period, we successfully submitted 20 deliverables, of which 15 were scientific in nature. These scientific deliverables included original research papers, experimental data, and new methodologies that contributed to the advancement of knowledge within the project's scope. The remaining five deliverables focused on non-scientific objectives, such as dissemination reports, training activities, and scientific publications.
We also achieved significant progress in key work packages, particularly in developing innovative solutions and validating our methodologies through testing and peer review. Additionally, several collaborative efforts with industry partners and academic institutions were initiated, which further strengthened the impact and applicability of our research outcomes.
Work performed in particular:
WP Processes: We build models and tools for work performance evaluation; developed methods for optimising earthmoving processes; Created business cases and solutions for robotisation of highly automated heavy duty machinery.
WP Machine: We desinged energy-efficient powertrain architectures for mobile machines
WP Control: We developed autonomous bucket filling methods using machine-learning techniques; We devised robot learning for control of a heavy duty crane; We developed perception, world representation and 3D positioning for autonomous machinery in dynamic outdoor environments
We also achieved significant progress in key work packages, particularly in developing innovative solutions and validating our methodologies through testing and peer review. Additionally, several collaborative efforts with industry partners and academic institutions were initiated, which further strengthened the impact and applicability of our research outcomes.
Work performed in particular:
WP Processes: We build models and tools for work performance evaluation; developed methods for optimising earthmoving processes; Created business cases and solutions for robotisation of highly automated heavy duty machinery.
WP Machine: We desinged energy-efficient powertrain architectures for mobile machines
WP Control: We developed autonomous bucket filling methods using machine-learning techniques; We devised robot learning for control of a heavy duty crane; We developed perception, world representation and 3D positioning for autonomous machinery in dynamic outdoor environments
Progress beyond state of the art:
We have advanced the field in work performance evaluation of excavation work; We have proposed novel methods for optimal material flow in construction site; we have significant contributions in the multidisciplinary field of heavy work machines, automation, computer science, and business creating novel business models for robotisation of highly automated heavy duty machinery. We have proposed several energy-efficient powertrain architectures and trade-off for mobile machines. Our solutions for automatic bucket filling of a wheel loader have achieved the highest productivity compared to existing solutions; Our control methods for crane control has simplified crane control and reduced RnD effort and time. We have significant contributions to perception, world representation and 3D positioning for autonomous machinery using Lidar and Radar sensors.
We have defined the impact of the project in the grant agreement as follows. Our EID action will
1. provided eight ESRs with timely knowledge and practical experience in the fields of automation and energy-efficient systems to guide the development of next generation HDM machinery.
2. Collaboration of key partners in this industry and related fields of research will: a) enhance the ESRs’ career perspectives; b) contribute to the future competiveness of an important European industry; and c) strengthen the innovation capacity in technologies that are also highly relevant to other sectors and industries.
We have advanced the field in work performance evaluation of excavation work; We have proposed novel methods for optimal material flow in construction site; we have significant contributions in the multidisciplinary field of heavy work machines, automation, computer science, and business creating novel business models for robotisation of highly automated heavy duty machinery. We have proposed several energy-efficient powertrain architectures and trade-off for mobile machines. Our solutions for automatic bucket filling of a wheel loader have achieved the highest productivity compared to existing solutions; Our control methods for crane control has simplified crane control and reduced RnD effort and time. We have significant contributions to perception, world representation and 3D positioning for autonomous machinery using Lidar and Radar sensors.
We have defined the impact of the project in the grant agreement as follows. Our EID action will
1. provided eight ESRs with timely knowledge and practical experience in the fields of automation and energy-efficient systems to guide the development of next generation HDM machinery.
2. Collaboration of key partners in this industry and related fields of research will: a) enhance the ESRs’ career perspectives; b) contribute to the future competiveness of an important European industry; and c) strengthen the innovation capacity in technologies that are also highly relevant to other sectors and industries.