Robotic process automation

Summary

Robotic process automation (RPA) is a form of business process automation that is based on software robots (bots) or artificial intelligence (AI) agents. It is sometimes referred to as software robotics (not to be confused with robot software). In traditional workflow automation tools, a software developer produces a list of actions to automate a task and interface to the back end system using internal application programming interfaces (APIs) or dedicated scripting language. In contrast, RPA systems develop the action list by watching the user perform that task in the application's graphical user interface (GUI), and then perform the automation by repeating those tasks directly in the GUI. This can lower the barrier to the use of automation in products that might not otherwise feature APIs for this purpose. RPA tools have strong technical similarities to graphical user interface testing tools. These tools also automate interactions with the GUI, and often do so by repeating a set o

Official source

https://en.wikipedia.org/wiki/Robotic_process_automation

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Robotic Automation and Unsupervised Cluster Assisted Modeling for Solving the Forward and Reverse Design Problem of Paper Airplanes

Josephine Anna Eleanor Hughes, Stefan Ilic, Kai Christian Junge, Nana Obayashi

2022

Augmented Robotics for Learners: A Case Study on Optics

Wafa Monia Benkaouar Johal, Amaury Robert Dame, Stéphane Magnenat, Francesco Mondada, Olguta Robu

In recent years, robots have been surfing on a trendy wave as standard devices for teaching programming. The tangibility of robotics platforms allows for collaborative and interactive learning. Moreover, with these robot platforms, we also observe the occurrence of a shift of visual attention from the screen (on which the programming is done) to the physical environments (i.e. the robot). In this paper, we describe an experiment aiming at studying the effect of using augmented reality (AR) representations of sensor data in a robotic learning activity. We designed an AR system able to display in real-time the data of the Infra-Red sensors of the Thymio robot. In order to evaluate the impact of AR on the learner's understanding on how these sensors worked, we designed a pedagogical lesson that can run with or without the AR rendering. Two different age groups of students participated in this between-subject experiment, counting a total of 74 children. The tests were the same for the experimental (AR) and control group (no AR). The exercises differed only through the use of AR. Our results show that AR was worth being used for younger groups dealing with difficult concepts. We discuss our findings and propose future works to establish guidelines for designing AR robotic learning sessions.

IEEE2019

Although recent advances in the field of robotics have greatly increased robotic capabilities for several applications, autonomous robots are still in their infancy regarding their support of onsite construction. Unlike production robots that are used, for instance, in automotive industries, autonomous robotic systems should be designed with special considerations, such as the complexity of the cluttered and dynamic working space, inaccuracy in positioning because of the nature of mobile systems, and so forth. Thus, construction has been known as a highly complex application field for robotic systems, especially in unknown environments. In this thesis, we focus on the developments of an autonomous construction system by taking inspiration from architectural designs found in the animal kingdom. The construction system allows robots to build functional structures over a different range of environmental conditions or in unknown sites without the use of any predefined construction plan (i.e., blueprint). We call this approach adaptive and functional autonomous construction (AFAC). Indeed, AFAC is a robust and intelligent system that can tackle unforeseen problems of unknown environments and faults made during the course of building. We present two approaches for AFAC. One is a local approach that enables robots to locally sense features of their environment and then act immediately according to defined rules without using any global representation or knowledge of the world. Second is a global approach, which involves building structures by analysing global representations and knowledge produced by the robots. This approach consists of three phases: the first phase is exploration, to map the unknown environment and to find important elements. In the second phase, an effective construction plan is autonomously computed using collected data and the defined functions. Finally, in the last phase, the robot builds structures based on the computed construction plan. One interesting implementation of the introduced construction system is its potential use in a post disaster environment, where an autonomous robotic system can perform construction tasks in the rescue operation. Robots can be employed to build protective structures for rescue functions, including stabilizing large structures or protecting victims in unknown environments, where the environment are perhaps covered by debris. Inasmuch as the robots cannot plan in advance to build these structures, the rescue application is a well suited case for the study and implementation of AFAC in unknown and cluttered environments. Through implementation of AFAC in the rescue situations, we develop AFAC from scratch to the level at which the robots can autonomously apply AFAC's principles for use in the new classes of construction applications. We also study and compare the local and global approaches of AFAC. In addition, we develop new exploration methods and construction plan computation methodologies for the global approach to efficiently lead AFAC.

EPFL2017