DIGITAL LIBRARY
REMOTE TRAINING METHOD TO ASSIST ROBOTIC ACTIVITY IN INDUSTRY
Universidade Federal do Amazonas (BRAZIL)
About this paper:
Appears in: INTED2024 Proceedings
Publication year: 2024
Pages: 6357-6364
ISBN: 978-84-09-59215-9
ISSN: 2340-1079
doi: 10.21125/inted.2024.1666
Conference name: 18th International Technology, Education and Development Conference
Dates: 4-6 March, 2024
Location: Valencia, Spain
Abstract:
Collaborative robots, commonly known as cobots, are innovating how humans and robots collaborate in shared workspaces. In physical industrial environments, for example, they communicate with other devices and help signal the end of activities on production lines. This addresses the operator's lack of competence in machine breakdowns, robot calibration problems, etc.

Although intelligent systems can already perceive and respond to their environment to implement a company's production efficiency, the models developed can still be improved and, furthermore, built to allow increased human-robot collaboration through the physical environment and digital remote access.

In this way, a remote training method is proposed that aims to help industrial robotic activities, and its main objective is to train and empower operators to have the autonomy to solve problems through digital remote access.

In this context, the structure of the proposed method was based on psychologist Lev Vygotsky's scaffolding theory. This theory helps students to understand educational content through the resources, tools, instructions, and activities used in the teaching-learning process. Therefore, a remote system with three (03) scaffolded micro-services was modeled and implemented.

The first service is a local server responsible for controlling the robot's remote activities. The second service is a cloud manager responsible for directing incoming messages to the local server. The third microservice is a unifying platform with an interface for robotic activities, which is responsible for training the operators' remote actions.

In short, the initial results show that the cloud-based remote robotic training method raises questions about the performance of the interaction capacity, time, and cost gains between man and robot. In addition, it was observed that few robotic platforms are using concepts aimed at educational theory. However, much remains to be done concerning the method of remote mechanical training. Thus, this proposal's initial advantage is the possibility of remotely training professionals in this industrial sector using well-defined robotics concepts through education theory.
Keywords:
Robotics, industrial training, engineering education.