Advanced Robot Programming: a Review

Zhou, Zhongxiang; Xiong, Rong; Wang, Yue; Zhang, Jiafan

doi:10.1007/s43154-020-00023-4

Cited by 14 publications

(8 citation statements)

References 51 publications

(47 reference statements)

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“…The advantage of these methods is that the correspondence mapping function can be omitted because all demonstration steps are performed in the robot configuration space. Nevertheless, difficulty in obtaining smooth and effective trajectories is increased due to the complexity faced by the demonstrator in manipulating the body of the robot, especially in robotic systems with high degrees of freedom [24,26].…”

Section: Data Acquisition Stagementioning

confidence: 99%

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: Data Acquisition Stagementioning

confidence: 99%

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

2022

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“…There is a worldwide effort to develop programming methods for industrial robots [ 37 , 38 , 39 ]. These methods can be divided into three groups [ 40 ]: online (non-text-based), offline (text-based, including graphical), hybrid (a combination of both methods).…”

Section: Related Workmentioning

confidence: 99%

Controlling an Industrial Robot Using a Graphic Tablet in Offline and Online Mode

Kaczmarek

Lotys²,

Borys

et al. 2021

Sensors

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The article presents the possibility of using a graphics tablet to control an industrial robot. The paper presents elements of software development for offline and online control of a robot. The program for the graphic tablet and the operator interface was developed in C# language in Visual Studio environment, while the program controlling the industrial robot was developed in RAPID language in the RobotStudio environment. Thanks to the development of a digital twin of the real robotic workstation, tests were carried out on the correct functioning of the application in offline mode (without using the real robot). The obtained results were verified in online mode (on a real production station). The developed computer programmes have a modular structure, which makes it possible to easily adapt them to one’s needs. The application allows for changing the parameters of the robot and the parameters of the path drawing. Tests were carried out on the influence of the sampling frequency and the tool diameter on the quality of the reconstructed trajectory of the industrial robot. The results confirmed the correctness of the application. Thanks to the new method of robot programming, it is possible to quickly modify the path by the operator, without the knowledge of robot programming languages. Further research will focus on analyzing the influence of screen resolution and layout scale on the accuracy of trajectory generation.

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“…Human-robot interaction (HRI) has made profilic use of augmented reality, as seen in recent review publications, such as in [14], which identifies a roadmap for the field. In the same way, in [15], a review of advanced robot programming approaches is presented where the authors explain the current augmented reality-based programming approaches.…”

Section: State Of the Artmentioning

confidence: 99%

Multimodal Mixed Reality Impact on a Hand Guiding Task with a Holographic Cobot

Pinto

Kildal

Lazkano

2020

MTI

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In the context of industrial production, a worker that wants to program a robot using the hand-guidance technique needs that the robot is available to be programmed and not in operation. This means that production with that robot is stopped during that time. A way around this constraint is to perform the same manual guidance steps on a holographic representation of the digital twin of the robot, using augmented reality technologies. However, this presents the limitation of a lack of tangibility of the visual holograms that the user tries to grab. We present an interface in which some of the tangibility is provided through ultrasound-based mid-air haptics actuation. We report a user study that evaluates the impact that the presence of such haptic feedback may have on a pick-and-place task of the wrist of a holographic robot arm.

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Advanced Robot Programming: a Review

Cited by 14 publications

References 51 publications

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

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

Controlling an Industrial Robot Using a Graphic Tablet in Offline and Online Mode

Multimodal Mixed Reality Impact on a Hand Guiding Task with a Holographic Cobot

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