A Survey on End-User Robot Programming

As robots interact with a broader range of end-users, end-user robot programming has helped democratize robot programming by empowering end-users who may not have experience in robot programming to customize robots to meet their individual contextual needs. This article surveys work on end-user robot programming, with a focus on end-user program specification. It describes the primary domains, programming phases, and design choices represented by the end-user robot programming literature. The survey concludes by highlighting open directions for further investigation to enhance and widen the reach of end-user robot programming systems.

Web Service for Point Cloud Supported Robot Programming Using Machine Learning

In industrial automation, the use of robots is already standard. But there is still a lot of room for further automation. One such place where improvements can be made is in the adjustment of a production system to new and unknown products. Currently, this task includes the reprogramming of the robot and a readjustment of the image processing algorithms if sensors are involved. This takes time, effort, and a specialist, something especially small and middle-sized companies shy away from. We propose to represent a physical production line with a digital twin, using the simulated production system to generate labeled data to be used for training in a deep learning component. An artificial neural network will be trained to both recognize and localize the observed products. This allows the production line to handle both known and unknown products more flexible. The deep learning component itself is located in a cloud and can be accessed through a web service, allowing any member of the staff to initiate the training, regardless of their programming skills. In summary, our approach addresses not only further automation in manufacturing but also the use of synthesized data for deep learning.

Playback Robot Programming Framework for Fiber Spraying Processes

Robot-based automation is still not widespread in small and medium-sized enterprises, since programming industrial robots is usually costly and only feasible by experts. This disadvantages can be resolved by using intuitive robot programming approaches like playback programming. At the same time, there are currently not automatized automatized, like fiber spraying. We present a novel approach in programming a robot system for fiber spraying processes, which extends a playback programming framework inspired by video editing concepts. The resulting framework allows the programming of also the periphery devices needed for the fiber spraying process. We evaluated the resulting programming framework to measure the intuitiveness in the use and show that the framework is not only able to program fiber spraying tasks but is also rather intuitive to use for domain experts.

Playback Robot Programming with Loop Increments

Playback robot programming is fast and easy to use for non-experts, because the robot only needs to be manually guided. However, it is only capable of replaying the trajectory exactly as it was taught. We present the concept of loop increments for playback programmed robots to allow the user to teach tasks like palletizing or stacking without having to explicitly guide the robot through each trajectory. Only the base trajectory for one repetition needs to be program med. After each loop iteration, the user-defined increment is added to the incremental configurations, e.g. to the pick or place configurations. To achieve this, two methods of defining the loop increments are shown. Afterwards, linear, Gaussian, and cosine blending functions in combination with the point and interval method are introduced for weighting the increments and as a foundation for the adaption algorithm. The evaluation showed, that the cosine blending function with the interval method best fits the needs of our programming system.

Crowdsourced Evaluation of Robot Programming Environments: Methodology and Application

Industrial robot programming tools increasingly rely on graphical interfaces, which aim at rendering the programming task more accessible to a wide variety of users. The usability of such tools is currently being evaluated in controlled environments, such as laboratories or companies, in which a group of participants is asked to carry out several tasks using the tool and then fill out a standardized questionnaire. In this context, this paper proposes and evaluates an alternative evaluation methodology, which leverages online crowdsourcing platforms to produce the same results as face-to-face evaluations. We applied the proposed framework in the evaluation of a web-based industrial robot programming tool called Assembly. Our results suggest that crowdsourcing facilitates a cost-effective, result-oriented, and reusable methodology for performing user studies anonymously and online.

Online programming system for robotic fillet welding in Industry 4.0

Purpose Fillet welding is one of the most widespread types of welding in the industry, which is still carried out manually or automated by contact. This paper aims to describe an online programming system for noncontact fillet welding robots with “U”- and “L”-shaped structures, which responds to the needs of the Fourth Industrial Revolution. Design/methodology/approach In this paper, the authors propose an online robot programming methodology that eliminates unnecessary steps traditionally performed in robotic welding, so that the operator only performs three steps to complete the welding task. First, choose the piece to weld. Then, enter the welding parameters. Finally, it sends the automatically generated program to the robot. Findings The system finally managed to perform the fillet welding task with the proposed method in a more efficient preparation time than the compared methods. For this, a reduced number of components was used compared to other systems: a structured light 3 D camera, two computers and a concentrator, in addition to the six-axis industrial robotic arm. The operating complexity of the system has been reduced as much as possible. Practical implications To the best of the authors’ knowledge, there is no scientific or commercial evidence of an online robot programming system capable of performing a fillet welding process, simplifying the process so that it is completely transparent for the operator and framed in the Industry 4.0 paradigm. Its commercial potential lies mainly in its simple and low-cost implementation in a flexible system capable of adapting to any industrial fillet welding job and to any support that can accommodate it. Originality/value In this study, a robotic robust system is achieved, aligned to Industry 4.0, with a friendly, intuitive and simple interface for an operator who does not need to have knowledge of industrial robotics, allowing him to perform a fillet welding saving time and increasing productivity.