iMRK: Demonstrator for Intelligent and Intuitive Human–Robot Collaboration in Industrial Manufacturing

Fernández, José de Gea; Mronga, Dennis; Günther, Martin; Wirkus, Malte; Schröer, Martin; Stiene, Stefan; Kirchner, Elsa Andrea; Bargsten, Vinzenz; Bänziger, Timo; Teiwes, Johannes; Krüger, Thomas; Kirchner, Frank

doi:10.1007/s13218-016-0481-5

Cited by 10 publications

(6 citation statements)

References 2 publications

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“…Further approaches on smart production include vision systems for observing the worker and interpreting gestures to control the robot with play or stop commands [22]. Also Wein et al have presented a camerabased system for object recognition in the workspace.…”

Section: Research Questions and Goalsmentioning

confidence: 99%

Plug, Plan and Produce as Enabler for Easy Workcell Setup and Collaborative Robot Programming in Smart Factories

Wojtynek

Steil²,

Wrede

2019

Künstl Intell

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The transformation of today's manufacturing lines into truly adaptive systems facilitating individualized mass production requires new approaches for the efficient integration, configuration and control of robotics and automation components. Recently, various types of Plug-and-Produce architectures were proposed that support the discovery, integration and configuration of field devices, automation equipment or industrial robots during commissioning or even operation of manufacturing systems. However, in many of these approaches, the configuration possibilities are limited, which is a particular problem if robots operate in dynamic environments with constrained workspaces and exchangeable automation components as typically required for flexible manufacturing processes. In this article, we introduce an extended Plug-and-Produce concept based on dynamic motion planning, co-simulation and a collaborative human-robot interaction scheme that facilitates the quick adaptation of robotics behaviors in the context of a modular production system. To confirm our hypothesis on the efficiency and usability of this concept, we carried out a feasibility study where participants performed a flexible workcell setup. The results indicate that the assistance and features for planning effectively support the users in tasks of different complexity and that a quick adaption is indeed possible. Based on our observations, we identify further research challenges in the context of Plug, Plan and Produce applied to smart manufacturing.

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Section: Research Questions and Goalsmentioning

confidence: 99%

Plug, Plan and Produce as Enabler for Easy Workcell Setup and Collaborative Robot Programming in Smart Factories

Wojtynek

Steil²,

Wrede

2019

Künstl Intell

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“…In all the experiments, the force was undesired; thus, the robot stopped when an external force was detected. De Gea Fernández et al [13] described another industrial situation in which two robotic arms collaborated with an operator. The robots avoided the physical interaction with the human as long as possible, and when a physical interaction occurred, they remained in a compliant mode so that the force was ignored.…”

Section: Related Workmentioning

confidence: 99%

On Inferring Intentions in Shared Tasks for Industrial Collaborative Robots

2019

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Inferring human operators’ actions in shared collaborative tasks plays a crucial role in enhancing the cognitive capabilities of industrial robots. In all these incipient collaborative robotic applications, humans and robots not only should share space, but also forces and the execution of a task. In this article, we present a robotic system that is able to identify different human’s intentions and to adapt its behavior consequently, only employing force data. In order to accomplish this aim, three major contributions are presented: (a) a force based operator’s intention recognition system based on data from only two users; (b) a force based dataset of physical human–robot interaction; and (c) validation of the whole system with 15 people in a scenario inspired by a realistic industrial application. This work is an important step towards a more natural and user-friendly manner of physical human–robot interaction in scenarios where humans and robots collaborate in the accomplishment of a task.

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“…He et al [27] analyzed the decomposition method for the industrial power demand. De Gea Fernández et al [6] introduced an intelligent and intuitive dual-arm robotic system for industrial human-robot collaboration. Hsieh [28] proposed a collaboration mechanism of resource donation, including unilateral resource donation and reciprocal resource donation.…”

Section: Related Workmentioning

confidence: 99%

“…Many researchers [2]- [4] have made great achievements regarding collaboration of ICSs. A lot of researchers have focused on the intelligent industrial ecosystem [5], [6], which handle the collection of massive data from the various devices by dynamical collaboration. However, they did not take into consideration the complex physical resources for the whole collaboration process.…”

Section: Introductionmentioning

confidence: 99%

A Decomposition-Based Development Method for Industrial Control Systems

Xiong

Shi

et al. 2019

IEEE Access

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Industrial control systems (ICSs), especially distributed control systems (DCSs), are usually composed of several subsystems. Each subsystem is controlled by a control unit such as a programmable logic controller (PLC) or a micro-controller and collaborates with other subsystems via the field bus, Ethernet, or other communication links. In the traditional development process, engineers program for each PLC separately and skillfully orchestrate the collaboration among subsystems, which is difficult and errorprone. The larger the scale of the ICS is, the higher the complexity of the collaboration is, and the more errorprone the development process is. In this paper, we propose a decomposition-based development method for distributed ICSs to reduce the difficulty of developing distributed ICSs whose subsystems cooperate with each other. First, we present a general event-triggered specification language named Industrial Modeling Collaboration Language (IMCL) for modeling ICSs; the language allows describing system functions and physical resources in one unified model. Second, we provide an approach for decomposing the complex system model into multiple fine-grained and interactive subsystem models. Specifically, under given resource constraints, we propose an automatic decomposition and collaboration algorithm based on the IMCL model to meet the original functional requirements. In this way, engineers can develop distributed control systems without considering the underlying complex interaction mechanisms. We present a case study to demonstrate it.

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iMRK: Demonstrator for Intelligent and Intuitive Human–Robot Collaboration in Industrial Manufacturing

Cited by 10 publications

References 2 publications

Plug, Plan and Produce as Enabler for Easy Workcell Setup and Collaborative Robot Programming in Smart Factories

Plug, Plan and Produce as Enabler for Easy Workcell Setup and Collaborative Robot Programming in Smart Factories

On Inferring Intentions in Shared Tasks for Industrial Collaborative Robots

A Decomposition-Based Development Method for Industrial Control Systems

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