Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
Session Overview
Session
SES 4.3: Collaborative Robotics in Smart Manufacturing
Time:
Wednesday, 28/Jun/2017:
9:00am - 10:00am

Session Chair: Pedro Neto
Location: Aula O (first floor)

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Presentations

89. Collaborative Robots in E-Waste Management

Esther Álvarez de los Mozos1, Arantxa Renteria2

1University of Deusto, Spain; 2Tecnalia Research & Innovation, Derio 48160, Spain

Nowadays manufacturing companies are going through an increasing public and government pressure to reduce the environmental impact of their operations. Customers are changing their attitude towards the products they buy considering not only the acquisition cost but also how the product was made and in which labour conditions, as well as how it is disposed at the end of its life cycle. There are recent examples such as the VW emissions scandal, variations in oil prices depending on the use of fracking technologies for extraction, or lack of raw materials for the manufacturing of high-tech devices causing environmental and social problems in third countries. They show that the consequences of ignoring the environmental effects of the manufacturing operations cause unexpected results in the acceptance of a product.

Until recently, and due to the economic crisis, the quest for higher employment rates seemed to fade the role of the circular economy, green manufacturing and use of recycled elements as source of raw materials. The aim was to encourage natural resource extraction and manufacturing activities regardless of their environmental impact, seeking for the reduction of unemployment. But what used to be taken as restrictions on economic activities in the past, is increasingly regarded as an essential element for the individual preferences and levels of quality of life, and thus emerges as new opportunities for development and improving competitiveness. Now the environment plays a core role in the people´s decisions: where to live, what to consume, how to produce.

The circular economy requires new production schemes, we cannot afford to waste scarce materials and resources. But when dealing with waste from electric and electronic equipment (WEEE), the barriers for the success of their recycling (technical and economic) are the difficulties in the classification and disassembly of components. The manual process is financially prohibitive and the full automation of the activity has not been achieved due to the lack of uniformity of the disposed devices. A halfway solution is to let a human operator and a robot share the process. New developments in collaborative robots allow for a close cooperation between humans and robots in a common working place, without fences between them. Thus the complexity of material and component identification could rely on the human side, while the more force demanding (and dangerous) tasks could be carried out by a robot. Current technical problems in the identification, classification, disassembling and manipulation of WEEE could be overwhelmed by a combination of robotized and manual operations, where the human teach a robot where to cut, separate parts, and the machine performs the low skilling operations. In addition, a smart transfer of tools and components must be achieved between human operator and robot.

The goal of this research is the optimization of the recycling process of electronic equipment, applying technical and economic criteria, taking into account new developments in collaborative robotics, and generating a model according to a dismantling strategy and degree of recovery that optimizes the profitability of the recycling.


78. An AR-based Worker Support System for Human-Robot Collaboration

Hongyi Liu, Lihui Wang

KTH-Royal Institute of Technology, Sweden

In human-robot collaborative manufacturing, industrial robots would work alongside the human workers who jointly perform the assigned tasks. Recent researches revealed that recognised human motions could be used as input for industrial robots control. However, the information feedback channel from industrial robots to human workers is still limited. In response to the requirement, this research explores the potential of adopting augmented reality (AR) technologies in a worker support system for human-robot collaborative manufacturing. The robot commands and worker instructions can be virtually augmented for human workers intuitively and instantly. The designed AR-based worker support system is demonstrated by a case study.


181. Human behavior and hand gesture classification for smart human-robot interaction

Nuno Marques Mendes, João Ferrer, João Vitorino, Mohammad Safeea, Pedro Neto

University of Coimbra, Portugal

This paper presents an intuitive human-robot interaction (HRI) framework for gesture and human behavior recognition. It relies on a vision-based system as interaction technology to classify gestures and a 3-axis accelerometer for behavior classification (stand, walking, etc.). An intelligent system integrates static gesture recognition recurring to artificial neural networks (ANNs) and dynamic gesture recognition using hidden Markov models (HMM). Results show a recognition rate of 95% for a library of 22 gestures and 97% for a library of 6 behaviors. Experiments show a robot controlled using gestures in a HRI process.



 
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