Have you ever wondered what our early stage researchers (ESRs) were are talking about when they mention optimised power management or what exactly perception sensors means? Have we ever used acronyms such as HDMM or BAGEL which just are not clear? Or have you ever wondered what the daily tasks or our ESRs are?
Our ESRs receive questions or are asked to explain parts of their research on a daily basis which may seem obvious to researchers in heavy duty machinery but for all us others can be overwhelming or difficult to follow. Based on this, the MORE ESRs have recorded the knowMORE series, a frequently asked question in video format answering the main FAQs they receive in an easy to follow way.
The second annual MORE network event was organised by Örebro University in Sweden from the 18.-20. May 2022. During the annual network events the focus is to give all the MORE early stage researchers the opportunity their research results and work progress and to obtain valuable input from their supervisors, industrial collaborator and external experts.
The annual events are always combined with other activities, such as technical sessions about relevant topics, onsite visits as well as social networking activities. During this annual event, we received a lab tour through the AASS lab at Örebro Universtiy as well as a visit to Munktell Museum. Time was further devoted to the training of the MORE early stage researchers from partners within the network. This included sessions on Communication, dissemination and exploitation activities in MORE: As open as possible, as closed as necessary (Julia Götz, accelopment); Towards robot-, task- and environment-agnostic multi-robot fleet control (Federico Pecora, Associate Professor at Örebro University); and Ethics for Robotics & Automation (Masoumeh Mansouri, Assistant Professor at the School of Computer Science, University of Birmingham).
We would like to thank Örebro University for hosting the meeting and for all participants for their active contributions and engagement during the meeting.
The MORE project has been running for nearly two years and due to travel restrictions, all meetings and trainings up to now have been held virtually until today. We finally had the opportunity to meet in person for the first time from the 15th-17th November 2021. In total, 23 members from the MORE consortium including the eight Early Stage Researchers met up for the 3rd Industry training event organised by TAU in Tampere, Finland.
The three-day training event included presentations from invited external speakers on Robotics, manipulator, trajectory optimization (Arun Singh, Univ. of Tartu) and a number of MORE network members on a number of relevant topics covering Hydraulics boom (Marcus Rösth, HIAB), Forestry Applications and Technologies (Timo Käppi, J. Deere), BIM in Infra Construction (Teppo Viinikka, Novatron), Control of Articulated Hydraulic Robotic Systems, (Janne Koivumäki Novatron), Operator assistance (Manuel Bös, Liebherr), Robot learning for heavy mobile machines (Nataliya Strokina / Reza Ghabcheloo,TAU) and Automation, Robotics in heavy machines (Christine Brach/Ulrich Lenzgeiger, Bosch Rexroth)
We also had the opportunity to visit our local hosts’ labs and demonstrators. TAU to showcase Robots in action in our RoboLab Tampere https://research.tuni.fi/robolabtampere/ At the end of each day, we held roundtable session to discuss the technical topics which were presented during the day. In these discussion academia and industry collaboration was intensively elaborated. Being able to meet in person has provided the MORE members the possibility to get to know each other on a much more personal level and to strengthen team spirit. We would like to thank all the contributors to the Industry Day and are looking forward to MORE!
Amirmasoud is one of the eight Early Stage Researchers in MORE who are all researching on individual projects to address the need for dramatic improvements in HDM machinery. In this video, Amirmasoud Molaei presents his individual project in MORE on work performance evaluation of mobile machines in earth moving tasks by using process models and sensor data.