Testing the vertical and cyber-physical integration of cognitive robots in manufacturing

Krüger, Volker; Rovida, Francesco; Großmann, Bjarne; Petrick, Ronald P. A.; Crosby, Matthew; Charzoule, Arnaud; García, Germán Martín; Behnke, Sven; Toscano, César; Veiga, Germano

doi:10.1016/j.rcim.2018.11.011

Cited by 38 publications

(13 citation statements)

References 9 publications

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“…At the control level, Mrugalska and Wyrwicka (2017) propose the use of the Internet of Things to ensure that the right products go to the right workstations and automatically redirect products in the event of referral errors. At the autonomy level, Krueger et al (2019) and Nikolakis, Maratos, and Makris (2019) use autonomous robots that detect and correct detected production errors themselves.…”

Section: Jidokamentioning

confidence: 99%

Impacts of Industry 4.0 technologies on Lean principles

Rosin

Forget

Lamouri

et al. 2019

International Journal of Production Research

335

205

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Industry 4.0 is increasingly being promoted as the key to improving productivity, promoting economic growth and ensuring the sustainability of manufacturing companies. On the other hand, many companies have already partially or fully implemented principles and tools from the Lean management approach, which is also aimed at improving productivity. While the two approaches use very different strategies, they share some common principles. The objective of this article is to highlight the links between the principles and tools proposed by Industry 4.0 and those proposed by the Lean management approach, with a particular focus on how some of Industry 4.0's technologies are improving the implementation of Lean principles, depending on the technologies' capability levels. As such, this study aims to provide a characterisation of the impacts of Industry 4.0 technologies on Lean principles according to targeted capability levels. The results obtained show strong support for Industry 4.0 technologies for Just-in-time and Jidoka, but little or no support for waste reduction and People and Team work. There is, therefore, a clear need to pursue the deployment of Lean management while improving certain Lean principles using Industry 4.0 technologies.

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Section: Jidokamentioning

confidence: 99%

Impacts of Industry 4.0 technologies on Lean principles

Rosin

Forget

Lamouri

et al. 2019

International Journal of Production Research

335

205

View full text Add to dashboard Cite

show abstract

“…However, this specific solution only enhances certain key steps in a specific decision-making process. Krueger et al [70] describe the STAMINA robot system in which Industry 4.0 technologies help to increase robots' autonomy for performing kitting tasks. These robots are integrated into the Manufacturing Execution System (MES) and operate in a shared workspace between humans and robots to handle abnormal situations spotted by sensors and a vision system.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing the Decision-Making Process through Industry 4.0 Technologies

et al. 2022

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In order to meet the increasingly complex expectations of customers, many companies must increase efficiency and agility. In this sense, Industry 4.0 technologies offer significant opportunities for improving both operational and decision-making processes. These developments make it possible to consider an increase in the level of operational systems and teams’ autonomy. However, the potential for strengthening the decision-making process by means of these new technologies remains unclear in the current literature. To fill this gap, a Delphi study using the Régnier Abacus technique was conducted with a representative panel of 24 experts. The novelty of this study was to identify and characterize the potential for enhancing the overall decision-making process with the main Industry 4.0 groups of technologies. Our results show that cloud computing appears as a backbone to enhance the entire decision-making process. However, certain technologies, such as IoT and simulation, have a strong potential for only specific steps within the decision-making process. This research also provides a first vision of the manager’s perspectives, expectations, and risks associated with implementing new modes of decision-making and cyber-autonomy supported by Industry 4.0 technologies.

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“…One way to fulfill these criteria is to use systems based on parameterized skills [1], [2], [3]. These encapsulated abilities realize semantically defined actions such as moving the robot arm, opening a gripper or localizing an object with vision.…”

Section: Introductionmentioning

confidence: 99%

Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration

Mayr¹,

Ahmad²,

Chatzilygeroudis³

et al. 2022

Preprint

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In modern industrial settings with small batch sizes it should be easy to set up a robot system for a new task. Strategies exist, e.g. the use of skills, but when it comes to handling forces and torques, these systems often fall short. We introduce an approach that provides a combination of task-level planning with targeted learning of scenario-specific parameters for skill-based systems. We propose the following pipeline: the user provides a task goal in the planning language PDDL, then a plan (i.e., a sequence of skills) is generated and the learnable parameters of the skills are automatically identified, and, finally, an operator chooses reward functions and hyperparameters for the learning process. Two aspects of our methodology are critical: (a) learning is tightly integrated with a knowledge framework to support symbolic planning and to provide priors for learning, (b) using multi-objective optimization. This can help to balance key performance indicators (KPIs) such as safety and task performance since they can often affect each other. We adopt a multi-objective Bayesian optimization approach and learn entirely in simulation. We demonstrate the efficacy and versatility of our approach by learning skill parameters for two different contact-rich tasks. We show their successful execution on a real 7-DOF KUKA-iiwa manipulator and outperform the manual parameterization by human robot operators.

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Testing the vertical and cyber-physical integration of cognitive robots in manufacturing

Cited by 38 publications

References 9 publications

Impacts of Industry 4.0 technologies on Lean principles

Impacts of Industry 4.0 technologies on Lean principles

Enhancing the Decision-Making Process through Industry 4.0 Technologies

Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration

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