Automatisation robotisée des processus

L'automatisation robotisée des processus (en anglais robotic process automation ou RPA) est une technologie de création de robots par apprentissage du comportement d'un usager sur une interface graphique. Dans une démarche classique d'automatisation de processus, un développeur informatique écrit un programme informatique qui effectue un certain nombre de tâches et interagit avec l'interface de programmation (API) de l'application. Dans une démarche d'automatisation robotisée des processus, le système apprend la liste des tâches à automatiser en observant le comportement d'utilisateurs humains. L'automatisation robotisée des processus est utilisée dans des entreprises pour automatiser des tâches comme valider la vente de primes d'assurance, traiter les demandes de règlement d'assurance maladie ou générer des factures de service public. Plus généralement, les tâches automatisées par ces robots correspondent à des actions bien précises effectuées sans réflexion ou avec des logiques trè

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

Functional and Adaptive Construction for Rescue

Hadi Ardiny

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

Functional and Adaptive Construction for Rescue

Hadi Ardiny

EPFL2017