Agency: Cordis | Branch: H2020 | Program: RIA | Phase: ICT-25-2016-2017 | Award Amount: 3.95M | Year: 2017
Recent technological progress in robot physical interaction permitted robots to actively and safely share with human a common workspace. Thanks to these technologies, Europe nowadays leads the robotic market in the niche of safety certified robots by endowing them with the ability to react to unintentional contacts. ANDY leverages these technologies and strengthen the European leadership by endowing robots with the ability to control physical collaboration through intentional interaction. These advances necessitate progresses along three main directions: measuring, modeling and helping humans engaged in intentional collaborative physical tasks. First, ANDY will innovate the way of measuring human whole-body motions developing the ANDYSUIT, a wearable force and motion tracking technology. Second, ANDY will develop the ANDYMODEL, a technology to learn cognitive models of human behavior in collaborative tasks. Third, ANDY will propose the ANDYCONTROL, an innovative technology for helping humans through predictive physical collaboration. ANDY will accelerate take-up and deployment by validating its progresses in realistic scenarios. In the first validation scenario the robot is identified with an industrial collaborative robot (i.e. robot=cobot) which adapts its ergonomy to individual workers. In the second validation scenario the robot is identified with an assistive exoskeleton (i.e. robot=exoskeleton) optimizing human comfort and reducing physical stress. In the third validation scenario the robot is identified with a humanoid (i.e. robot=humanoid) offering assistance to a human while maintaining the balance of both.
Agency: Cordis | Branch: FP7 | Program: CP | Phase: ICT-2013.1.3 | Award Amount: 4.91M | Year: 2013
New methods and tools for the design of production systems should be used in order to provide advanced services to the industry shop-floor personnel in terms of ergonomics, safety, efficiency, complexity management and work satisfaction. In the field of planning and verifying human-centred assembly workplaces, available simulation and sensor technologies should be further improved. In many cases, task execution at shop-floor differs from the planned process.\nThe main idea of the project is to utilize workers knowledge on executing manual assembly tasks and include it in the digital tools used to support design, verification, validation, modification and continuous improvement of human-centred, flexible assembly workplaces. This kind of knowledge will be retrieved from workers actions using low-cost and non-intrusive sensor and tracking technology that can be applied during assembly process execution and will be used for improving the precision and accuracy of digital human process simulation tools, thus achieving faster ramp-ups and first-time-right assembly processes.\n Shop-floor sensing architectures that incorporate new low-cost sensor systems for retrieving real-time data about human-based work activities.\n Advanced methods and tools for the automated recognition and classification of assembly operations from sensors data.\n Efficient generation and modification of accurate manual assembly simulation models. Methodology for reliable, cost-effective and fast ways to generate manual assembly operation models from semantic task description and sensor data.\n Development of smart applications for continuous improvement of the human-centered manual assembly workplace. Shop-floor applications will be deployed as apps through an Enterprise Application Store, following the smartphone and tablet device paradigm.\nA demonstration phase will apply the prototypical results in two complementary industrial pilot cases (automotive industry, professional white goods)
Fritzsche L.,imk automotive GmbH |
Wegge J.,TU Dresden |
Schmauder M.,TU Dresden |
Kliegel M.,University of Geneva |
Schmidt K.-H.,TU Dortmund
Ergonomics | Year: 2014
Prior research suggests that ergonomics work design and mixed teams (in age and gender) may compensate declines in certain abilities of ageing employees. This study investigates simultaneous effects of both team level factors on absenteeism and performance (error rates) over one year in a sample of 56 car assembly teams (N = 623). Results show that age was related to prolonged absenteeism and more mistakes in work planning, but not to overall performance. In comparison, high-physical workload was strongly associated with longer absenteeism and increased error rates. Furthermore, controlling for physical workload, age diversity was related to shorter absenteeism, and the presence of females in the team was associated with shorter absenteeism and better performance. In summary, this study suggests that both ergonomics work design and mixed team composition may compensate age-related productivity risks in manufacturing by maintaining the work ability of older employees and improving job quality. Practitioner Summary: The ageing workforce is considered as productivity risk in manufacturing industries. This study shows that high-physical workloads and homogeneous team composition are both associated with higher absenteeism and error rates. Thus, practitioners are prompted to reduce ergonomics risks in production and introduce age- and gender-mixed teams to sustain productivity. © 2014 © 2014 Taylor & Francis.
Roscher M.A.,Imk Automotive GmbH |
Bohlen O.S.,BMW AG |
Sauer D.U.,RWTH Aachen
IEEE Transactions on Energy Conversion | Year: 2011
Lithium-ion (Li-ion) batteries are nowadays the best tradeoff between performance, cost, and lifetime to store electric power and energy effectively. The reliable determination of the batteries instantaneous power capability and energy content is mandatory for many mobile high-energy and power-consuming applications. Herein, the battery impedance and state of charge (SOC) are the relevant values. In energy storage systems, these values are to be considered for each battery cell individually, due to arising limitations caused by cell-specific variations. Simple algorithms to determine the battery systems impedance and SOC are presented including parameter and state-estimation techniques. Furthermore, methods are derived making the impedance and SOC determination possible for a large number of particular cells in a battery system. The applicability of the demonstrated algorithms for battery control unit implementation is proved incorporating data achieved from load scenarios applied to a cell stack Li-ion battery module. Cell impedance and SOC variations could be detected precisely. Moreover, a combination of impedance and SOC spread was identified during typical battery operation. © 2011 IEEE.
Roscher M.A.,Imk Automotive GmbH |
Michel R.,Imk Automotive GmbH |
Leidholdt W.,Imk Automotive GmbH
International Journal of Vehicular Technology | Year: 2013
The limited amount of energy stored on board of battery electric vehicles (BEV) spurs research activities in the field of efficiency optimization for electric drive train applications in order to achieve an enhanced mileage. In this work a control method for BEV applications with two drive trains (e.g., one at the front and one at the rear axle) is presented. Herein, a simple optimization algorithm is introduced enabling to operate the two drives with different torque values, depending on the instantaneous operation point, leading to a reduction of apparent power losses on board. Simulations on a virtual BEV yield a decrease in the cumulated energy consumptions during typical BEV operation, leading to an increase in the achievable mileage. © 2013 Michael A. Roscher et al.
Roscher M.A.,Imk Automotive GmbH |
Leidholdt W.,Imk Automotive GmbH |
Trepte J.,Imk Automotive GmbH
International Journal of Electrical Power and Energy Systems | Year: 2012
Battery electric vehicles (BEVs) are promising candidates to replace cars including ICE drive trains in the coming years especially in urban regions in order to contribute to a reduced exhaust and noise emission. Up to now the BEV's bottleneck is the battery system which is able to store only a very limited amount of energy on board. Hence, it is necessary to use the rare energy in the most efficient way. In this work a simple method is presented to reduce the electric losses during operation through an adaptive control of the HVAC power input, depending on the driving situation. It is shown that the proposed method enables an energy saving and therefore a range extension about more than 1% without any additional hardware effort. This basically does not seem to be much but can be an important step contributing to BEVs' final breakthrough. © 2011 Elsevier Ltd. All rights reserved.
Fritzsche L.,imk Automotive Inc. |
Jendrusch R.,imk Automotive GmbH |
Leidholdt W.,imk Automotive GmbH |
Bauer S.,imk Automotive GmbH |
And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011
The aging workforce is a risk factor for manufacturing industries that contain many jobs with high physical workloads. Thus, ergonomic risk factors have to be avoided in early phases of production planning. This paper introduces a new tool for simulating manual work activities with 3D human models, the so-called ema. For the most part, the ema software is based on a unique modular approach including a number of complex operations that were theoretically developed and empirically validated by means of motion capturing technologies. Using these modules for defining the digital work process enables the production planner to compile human simulations more accurately and much quicker compared to any of the existing modeling tools. Features of the ema. software implementation, such as ergonomic evaluation and MTM-time analyses, and the workflow for practical application are presented. © 2011 Springer-Verlag.
Fritzsche L.,Imk automotive GmbH
Human Factors and Ergonomics In Manufacturing | Year: 2010
This study investigated how well ergonomics risk assessments on simulations with digital human models (DHM)match real-life assessments obtained on a car assembly line. Two ergonomists evaluated 20 work tasks in real life and as a DHMsimulation using a company-specific version of the Automotive AssemblyWorksheet (AAWS) for assessing static postures, action forces,manualmaterial handling, and extra strains. Results demonstrate that DHM simulations provide good estimations of the workload in real-life tasks. Additionally, significant correlations were found between AAWS risk assessments and subjectively perceived exertion measured on the Borg scale. Yet, there were also some significant differences in AAWS risk classification and AAWS total scores. DHM simulations appear helpful for reliably detecting static postures and extra strains, whereas action forces are harder to estimate than in real life. It is suggested that comprehensive methods such as AAWS should be incorporated in DHM software for enhancing efficiency and validity of digital ergonomics risk assessment. © 2010 Wiley Periodicals, Inc.
Glaser D.,Imk Automotive GmbH |
Fritzsche L.,Imk Automotive GmbH |
Bauer S.,Imk Automotive GmbH |
Sylaja V.J.,Imk Automotive GmbH
Procedia CIRP | Year: 2016
The digital factory with its innovative tools is experiencing an increasing importance, not only in experimental but also productive domains. One of these tools is the digital human model (DHM). In the field of production, the focus of using DHMs lies in the planning and evaluation of processes and products in terms of plausibility, productivity and ergonomics. Up to now, ergonomic assessment within DHM simulations have been mostly limited to static evaluations of reachability and postures. INTERACT is a running R&D project, working on the main weak points of DHM software tools. The industry-driven requirements are mainly the reduction of input effort, the increase of movement quality and a quick and intuitive way to create simulation variations in a workshop environment. The utilization of sensor data to create high quality simulations is another point of development. Next to the addressed improvement in productivity and plausibility, these latest advancements also enable automatic ergonomic assessments, including process oriented standards like EAWS, OCRA and NIOSH lifting index. The inclusion of these standards will allow a more holistic ergonomic assessment and therewith expand the fields of application in the industrial environment. This paper will give an insight in the latest developments and the performance of current implementations of automatic ergonomic assessment within digital human models. © 2016 The Authors.
Fritzsche L.,Imk Automotive Inc. |
Leidholdt W.,Imk Automotive GmbH |
Bauer S.,Imk Automotive GmbH |
Jackel T.,Imk Automotive GmbH |
Moreno A.,Imk Automotive GmbH
Work | Year: 2012
The aging workforce is a risk factor for manufacturing industries that contain many jobs with high physical workloads. Thus, ergonomic risk factors have to be avoided in early phases of production planning. This paper introduces a new tool for simulating manual work activities with 3D human models, the so-called ema. For the most part, the ema software is based on a unique modular approach including a number of complex operations that were theoretically developed and empirically validated by means of motion capturing technologies. Using these modules for defining the digital work process enables the production planner to compile human simulations more accurately and much quicker compared to any of the existing modeling tools. Features of the ema software implementation, such as ergonomic evaluation and MTM-time analyses, and the workflow for practical application are presented. © 2012 - IOS Press and the authors. All rights reserved.