Why is MORE needed?
Robotisation/AI, energy efficiency, process optimisation are critical for innovation.
1) Robotisation of HDM machines and AI is expected to increase productivity and improve performance, work quality and safety, and reduce development costs. New robotics/AI techniques, from surround sensing to robot learning, will pave the way towards fully autonomous mobile machines. For example, the productivity of excavation tasks could increase by 12% if collision avoidance and assisting guidance systems are added. In addition, operational costs could be reduced by 15% using driverless trucks in mines. A labour shortage is already a serious issue, and mostly because of hazardous and inconvenient working environments, competitive costs and changes in the age demographic. MORE will develop object classification and world modelling to support long-term autonomy; obstacle avoidance in adverse conditions; innovative solutions on transfer learning for earth moving and crane control to reduce operational and development costs.
2) Energy efficiency is a big challenge as energy prices are increasing and regulations on emissions are tightening. Energy efficiency is important for both diesel powered and electric powered machines. In the diesel engine powered machines the average diesel usage is high – from 15 litres per hour by excavators to 60 litres per hour by scrapers representing 30 – 60 per cent of total cost of ownership. In the electric powered machines the battery capacity is directly de-pending on the system efficiency, lower the system efficiency is the bigger and more expensive the battery should be. Poorly operated machine can consume 50% more fuel than the same job accomplished by an experienced operator. However, operator training is expensive and there is a lack of skilled operators – a situation that is continuing to worsen. Besides efficacies gained due to robotisation and process optimization, MORE will create novel powertrain solutions for hydraulic booms/cranes, the least efficient and high consuming subsystem.
3) Optimisation of processes, e.g. in construction (Liebherr, VCE, Novatron) or logistics (HIAB), is the ultimate factor for gain. The optimality is affected by what functions or machines are automated, the performance of each machine and the interactions between different machines. If any of these factors are not selected optimally or not working at an optimal level, productivity is affected. MORE will investigate both the economic benefits of autonomous machines as well as the optimisation of the processes where these machines are used. In particular, MORE will improve world modelling by combining machine-earth interaction with vision. It will as well optimize and coordinate material flow in construction sites. Europe’s HDM machinery industry needs to invest heavily in digitalization, ‘green’ and innovative technologies. Thus, there is a rapidly growing demand for experts in HDM machinery who possess a big picture of the trends in robotics, AI and energy systems, and an understanding of the challenges and opportunities, and who are specialised in certain disciplines related to processes (field knowledge), machines (energy systems, transmissions), and robotics and AI (control and computer science). The MORE EID will address this need and fill the gap in related research and training.
 Nicole Elflein (Siemens mining), 2014. ‘Digging for efficiency’. Pictures of the Future, online magazine, Spring 2014.
 B. Frank, 2018. ‘On Optimal Control for Concept Evaluation and System Development in Construction Machines’, PhD thesis, Lund University.