Smart hardware integration with advanced robot programming technologies for efficient reconfiguration of robot workcells

Gašpar, Timotej; Deniša, Miha; Radanovic, Primoz; Ridge, Barry; Savarimuthu, Thiusius Rajeeth; Kramberger, Aljaž; Priggemeyer, Marc; Roßmann, Jürgen; Wörgötter, Florentin; Ivanovska, Tatyana; Parizi, Shahab; Gosar, Žiga; Kovac, Igor; Ude, Aleš

doi:10.1016/j.rcim.2020.101979

Cited by 50 publications

(27 citation statements)

References 26 publications

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“…Consequently, even small disturbances in the DMP (e.g. the DMP integration noise) might result in unstable trajectory generation 2 . It follows that we need to keep ν(s) positive in order to ensure the stability of the system.…”

Section: Reversible Cartesian Space Dmpsmentioning

confidence: 99%

“…Implementation on Universal Robot UR10 On the 6 DOF Universal Robot UR10, the control algorithm was implemented in Simulink using XPC Target platform, while the LfD framework was implemented in Python as a ROS node [2]. Wrist-mounted force-torque sensor ATI Delta was applied in this setup to implement admittance-based incremental learning.…”

Section: Implementation On Franka Emika Pandamentioning

confidence: 99%

“…The global competitive arena forces companies to react quickly to the demands of the market while maintaining economic feasibility. Consequently, the robotized production plants should adapt to handle a wide variety of products by frequent reconfiguration of both hardware sub-components and robot programs [2]. Another issue is to shorten the robot programming time and reduce the required skill level of the operators [3].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Methods for Incremental Policy Refinement by Kinesthetic Guidance

Simonič

Petrič

Ude

et al. 2021

J Intell Robot Syst

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Traditional robot programming is often not feasible in small-batch production, as it is time-consuming, inefficient, and expensive. To shorten the time necessary to deploy robot tasks, we need appropriate tools to enable efficient reuse of existing robot control policies. Incremental Learning from Demonstration (iLfD) and reversible Dynamic Movement Primitives (DMP) provide a framework for efficient policy demonstration and adaptation. In this paper, we extend our previously proposed framework with improvements that provide better performance and lower the algorithm’s computational burden. Further, we analyse the learning stability and evaluate the proposed framework with a comprehensive user study. The proposed methods have been evaluated on two popular collaborative robots, Franka Emika Panda and Universal Robot UR10.

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Section: Reversible Cartesian Space Dmpsmentioning

confidence: 99%

Section: Implementation On Franka Emika Pandamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of Methods for Incremental Policy Refinement by Kinesthetic Guidance

Simonič

Petrič

Ude

et al. 2021

J Intell Robot Syst

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“…Such components can be hardware or software. A robot cell based on the RMS paradigm using ROS is shown in [31]. As described in Section IV, ROS does not perform as well as OPC UA, and the semantic expressiveness regarding information exchange is limited.…”

Section: Related Workmentioning

confidence: 99%

A Generic Plug & Produce System Composed of Semantic OPC UA Skills

Profanter

Perzylo

Rickert

et al. 2021

IEEE Open J. Ind. Electron. Soc.

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Typical industrial workcells are composed of a plenitude of devices from various manufacturers, which rely on their own specific control interfaces. To reduce setup and reconfiguration times, a hardwareagnostic Plug & Produce system is required. In this paper, we present a system architecture that uses generic and semantically augmented OPC UA skills for robots, tools, and other system components. Standardized skill interfaces and parameters facilitate flexible component interchange and automatic parametrization with a focus on reusability of skills across different platforms and domains. The hierarchical composition of such skills allows for additional abstraction through the grouping of functionalities. Through the extension of OPC UA discovery services, available skills are dynamically detected whenever a manufacturing system's component is updated. The introduced Plug & Produce system is evaluated in multiple industrial workcells composed of robots, tool changer, electric parallel gripper, and vacuum gripper-all controlled via the proposed OPC UA skill interface. The evaluation of our system architecture demonstrates the applicability of the Plug & Produce concept in the domain of robot-based industrial assembly. Although it is necessary to adapt existing hardware to comply with the semantic skill concept, the initial one-time effort yields reoccurring efficiency gains during system reconfiguration. In particular, small lot production benefits from reduced changeover times.

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“…It is a paradigm in which robots learn new skills through imitating human operators' motions. Learning from demonstration methods present opportunities to advance beyond standard methodologies for robot programming, such as script-based method and simulation [5]. Acquiring human assembly motion is the first step in a typical LfD framework [6].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Optical Motion Capture Sensors for Assembly Motion Capturing

Cao

Yang

et al. 2021

IEEE Access

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The optical motion capture (MoCap) sensor provides an effective way to capture human motions and transform them into valuable data that can be applied to certain tasks, e.g. robot learning from demonstration (LfD). In spite of the wide utilization of optical MoCaps in LfD studies, there are few works that explore their potentiality in small parts robotic assembly. Robot manipulation skill learning from demonstration has gained the attention of researchers recently and robotic 3C (Computer, Communication, and Consumer Electronics) product assembly turns out to be a promising application thanks to the increasing consumption of 3C products. To further explore the potential of optical MoCaps in robotic 3C product assembly. This work proposes a performance evaluation protocol that takes the characters of both optical MoCaps and 3C product assembly operations into account. The proposed evaluation protocol includes static and trajectory evaluations. The former refers to the widely used evaluation indicators such as precision and accuracy. Meanwhile, the trajectory evaluation takes contour error as an error metric. Three popular optical MoCaps are studied in the experiment. Experiment results show that the static performance of all of the three optical MoCaps can meet the requirements of the 3C product assembly task. What's more, Prime X41 possesses the best trajectory performance. This work sheds light on the wider usage of optical MoCaps in manufacturing industries.

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Smart hardware integration with advanced robot programming technologies for efficient reconfiguration of robot workcells

Cited by 50 publications

References 26 publications

Analysis of Methods for Incremental Policy Refinement by Kinesthetic Guidance

Analysis of Methods for Incremental Policy Refinement by Kinesthetic Guidance

A Generic Plug & Produce System Composed of Semantic OPC UA Skills

Performance Evaluation of Optical Motion Capture Sensors for Assembly Motion Capturing

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