Operator - mobile robot collaboration for synchronized part movement

Ramasubramanian, Aswin K; Papakostas, Nikolaos

doi:10.1016/j.procir.2020.05.228

Cited by 19 publications

(9 citation statements)

References 10 publications

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“…The use of MoMas, however, goes beyond the walls of the laboratory when it comes to the pharmaceutical industry. For the Marvin Project of the PM Group and the University College Dublin, an iiwa unit was used for environmental monitoring purposes [57,58] .…”

Section: Moma Technologiesmentioning

confidence: 99%

Towards robotic laboratory automation Plug & Play: The “LAPP” framework

et al. 2022

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Section: Moma Technologiesmentioning

confidence: 99%

Towards robotic laboratory automation Plug & Play: The “LAPP” framework

et al. 2022

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“…The use of AMRAs, however, goes beyond the walls of the laboratory when it comes to the pharmaceutical industry. For the Marvin Project of the PM Group and the University College Dublin, an iiwa unit was used for environmental monitoring purposes 46,47 .…”

Section: Amra Technologiesmentioning

confidence: 99%

Towards Robotic Laboratory Automation Plug & Play: The "LAPP" Framework

Wolf,

Wolton,

Trapl

et al. 2021

Preprint

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Increasing the level of automation in pharmaceutical laboratories and production facilities plays a crucial role in delivering medicine to patients. However, the particular requirements of this field make it challenging to adapt cutting-edge technologies present in other industries. This article provides an overview of relevant approaches and how they can be utilized in the pharmaceutical industry, especially in development laboratories. Recent advancements include the application of flexible mobile manipulators capable of handling complex tasks. However, integrating devices from many different vendors into an end-to-end automation system is complicated due to the diversity of protocols. Therefore, various approaches for standardization have been considered, and a concept has been proposed for taking them a step further. This concept enables a mobile manipulator with a vision system to "learn" the pose of each device and -utilizing a barcode -fetch interface information from a universal cloud database. This information includes control and communication protocol definitions and a representation of robot actions needed to operate the device. In order to define the movements in relation to the device, devices have to feature -besides the barcode -a fiducial marker as standard. The concept will be elaborated following appropriate research activities in follow-up papers.

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“…The main goal would be to allow vision-based human tracking technologies to complement and work synergistically with built-in safety sensors that commercially available collaborative robots carry in order to overcome unforeseen circumstances to carry out complex tasks for instance shown in Ref. [ 15 ]. The identification or monitoring of specific safety features is required in industrial applications as per ISO/TS 15066 [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

On the Evaluation of Diverse Vision Systems towards Detecting Human Pose in Collaborative Robot Applications

Ramasubramanian,

Kazasidis,

Fay

et al. 2024

Sensors

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Tracking human operators working in the vicinity of collaborative robots can improve the design of safety architecture, ergonomics, and the execution of assembly tasks in a human–robot collaboration scenario. Three commercial spatial computation kits were used along with their Software Development Kits that provide various real-time functionalities to track human poses. The paper explored the possibility of combining the capabilities of different hardware systems and software frameworks that may lead to better performance and accuracy in detecting the human pose in collaborative robotic applications. This study assessed their performance in two different human poses at six depth levels, comparing the raw data and noise-reducing filtered data. In addition, a laser measurement device was employed as a ground truth indicator, together with the average Root Mean Square Error as an error metric. The obtained results were analysed and compared in terms of positional accuracy and repeatability, indicating the dependence of the sensors’ performance on the tracking distance. A Kalman-based filter was applied to fuse the human skeleton data and then to reconstruct the operator’s poses considering their performance in different distance zones. The results indicated that at a distance less than 3 m, Microsoft Azure Kinect demonstrated better tracking performance, followed by Intel RealSense D455 and Stereolabs ZED2, while at ranges higher than 3 m, ZED2 had superior tracking performance.

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Operator - mobile robot collaboration for synchronized part movement

Cited by 19 publications

References 10 publications

Towards robotic laboratory automation Plug & Play: The “LAPP” framework

Towards robotic laboratory automation Plug & Play: The “LAPP” framework

Towards Robotic Laboratory Automation Plug & Play: The "LAPP" Framework

On the Evaluation of Diverse Vision Systems towards Detecting Human Pose in Collaborative Robot Applications

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