Towards robot cell matrices for agile production – SDU Robotics' assembly cell at the WRC 2018

Schlette, Christian; Buch, Anders Glent; Hagelskjær, Frederik; Iturrate, Iñigo; Kraft, Dirk; Kramberger, Aljaž; Lindvig, Anders Prier; Mathiesen, Simon; Petersen, Henrik Gordon; Rasmussen, Mads Høj; Savarimuthu, Thiusius Rajeeth; Sloth, Christoffer; Sørensen, Lars Carøe; Thulesen, Thomas Nicky

doi:10.1080/01691864.2019.1686422

Cited by 12 publications

(5 citation statements)

References 7 publications

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“…The framework also uses a visual perception system to recognize object and certain environment properties to learn high-level conditions to encode symbolic action models. Schlette et al [54] also exploit the capabilities of the DMPs that allow the reduction of the recorded trajectories to a few parameters that can be modulated to adapt to new situations during execution arising as a result of time independence and encoding of forces and torques in the desired trajectories. Both works were developed in semi-structured environments and the main goal of teaching low-level skills is to use them to solve more complex tasks.…”

Section: Skill Learningmentioning

confidence: 99%

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Learning from Demonstrations in Human–Robot Collaborative Scenarios: A Survey

2022

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Human–Robot Collaboration (HRC) is an interdisciplinary research area that has gained attention within the smart manufacturing context. To address changes within manufacturing processes, HRC seeks to combine the impressive physical capabilities of robots with the cognitive abilities of humans to design tasks with high efficiency, repeatability, and adaptability. During the implementation of an HRC cell, a key activity is the robot programming that takes into account not only the robot restrictions and the working space, but also human interactions. One of the most promising techniques is the so-called Learning from Demonstration (LfD), this approach is based on a collection of learning algorithms, inspired by how humans imitate behaviors to learn and acquire new skills. In this way, the programming task could be simplified and provided by the shop floor operator. The aim of this work is to present a survey of this programming technique, with emphasis on collaborative scenarios rather than just an isolated task. The literature was classified and analyzed based on: the main algorithms employed for Skill/Task learning, and the human level of participation during the whole LfD process. Our analysis shows that human intervention has been poorly explored, and its implications have not been carefully considered. Among the different methods of data acquisition, the prevalent method is physical guidance. Regarding data modeling, techniques such as Dynamic Movement Primitives and Semantic Learning were the preferred methods for low-level and high-level task solving, respectively. This paper aims to provide guidance and insights for researchers looking for an introduction to LfD programming methods in collaborative robotics context and identify research opportunities.

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Section: Skill Learningmentioning

confidence: 99%

“…The usability and simplicity of the framework were tested with the help of multiple users with favorable results. In [54], a development was presented in a robotic assembly cell during the World Robot Challenge 2018 (WRC 2018) where operations such as pick and place, handling and screwing were evaluated.…”

Section: Skill Learningmentioning

confidence: 99%

Learning from Demonstrations in Human–Robot Collaborative Scenarios: A Survey

2022

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“…The tool consists of an electric gripper (1) to handle, manipulate and position the timber pieces with controllable finger stroke and gripping force, an automatic screwdriver (2) with an exchangeable screw tip and torque-based sensors. The screwdriver is mounted on a custom-made tool flange (3) perpendicular to the gripper [33] in order not to obstruct the gripper during its operation, and at the same time eliminating the need for time-consuming tool exchange processes. Additionally, a customized screw holder with an integrated magnet (4) was designed, 3D printed and attached to the screwdriver bit (5) in order to handle and insert M4 steel bolts.…”

Section: Robotic Assembly Of Reversible Timber Structures 41 Robot Setupmentioning

confidence: 99%

Design and assembly automation of the Robotic Reversible Timber Beam

Kunic

Naboni

Kramberger

et al. 2021

Automation in Construction

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“…For WRC 2018, team O2AS [11] developed a system with multiple robots to manage the taskspace more efficiently. WRC 2018's winning team from SDU introduced the concept of "robot cell matrices", which are reconfigurable and adaptable units of production or assembly, as part of their implementation [12]. They rely heavily on task-specific solutions like holders and fixtures, and rapid manufacturing techniques like 3D printing to cope with changes in requirements.…”

Section: Related Workmentioning

confidence: 99%

Lessons Learned Developing an Assembly System for WRS 2020 Assembly Challenge

Naik,

Parashar,

et al. 2021

Preprint

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The World Robot Summit (WRS) 2020 Assembly Challenge is designed to allow teams to demonstrate how one can build flexible, robust systems for assembly of machined objects. We present our approach to assembly based on integration of machine vision, robust planning and execution using behavior trees and a hierarchy of recovery strategies to ensure robust operation. Our system was selected for the WRS 2020 Assembly Challenge finals based on robust performance in the qualifying rounds. We present the systems approach adopted for the challenge.

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Towards robot cell matrices for agile production – SDU Robotics' assembly cell at the WRC 2018

Cited by 12 publications

References 7 publications

Learning from Demonstrations in Human–Robot Collaborative Scenarios: A Survey

Learning from Demonstrations in Human–Robot Collaborative Scenarios: A Survey

Design and assembly automation of the Robotic Reversible Timber Beam

Lessons Learned Developing an Assembly System for WRS 2020 Assembly Challenge

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