Human-Robot Collaboration as a new paradigm in circular economy for WEEE management

E-waste is a priority waste stream as identified by the European Commission due to fast technological changes and eagerness of consumers to acquire new products. The value chain of the Waste on Electric and Electronic Equipment (WEEE) has to face several challenges: the EU directives requesting collection targets for 2019–2022, the costs of disassembly processes which is highly dependent on the applied technology and type of discarded device, and the sale of the obtained components and/or raw materials, with market prices varying according to uncontrolled variables at world level. This paper presents a human-robot collaboration for a recycling process where tasks are opportunistically assigned to either a human-being or a robot depending on the condition of the discarded electronic device. This solution presents some important advantages; i.e. tedious and dangerous tasks are assigned to robots whereas more value-added tasks are allocated to humans, thus preserving jobs and increasing job satisfaction. Furthermore, first results from a prototype show greater productivity and profitable projected investment

Collaborative Robots in e-waste Management

Nowadays manufacturing companies are going through an increasing public and government pressure to reduce the environmental impact of their operations. But when dealing with e-waste, some difficulties arise in classifying and dismantling electronic devices. Manual operations are financially prohibitive and full automation is also discarded 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. The goal of this research is the optimization of the recycling process of electronic equipments, applying both technical and economic criteria, and taking into account the latest developments in collaborative robots

A Benchmark Dataset for Human Activity Recognition and Ambient Assisted Living

We present a data benchmark for the assessment of human activity recognition solutions, collected as part of the EU FP7 RUBICON project, and available to the scientific community. The dataset provides fully annotated data pertaining to numerous user activities and comprises synchronized data streams collected from a highly sensor-rich home environment. A baseline activity recognition performance obtained through an Echo State Network approach is provided along with the dataset.European Commission's FP

Robotic UBIquitous COgnitive Network

3rd International Symposium on Ambient Intelligence, Salamanca, Spain, 2012Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them self-adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The EU FP7 project RUBICON develops self-sustaining learning solutions yielding cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, agent control systems, wireless sensor networks and machine learning. This paper briefly illustrates how these techniques are being extended, integrated, and applied to AAL applications.TS 23.05.1

Self-sustaining learning for robotic ecologies

The most common use of wireless sensor networks (WSNs) is to collect environmental data from a specific area, and to channel it to a central processing node for on-line or off-line analysis. The WSN technology, however, can be used for much more ambitious goals. We claim that merging the concepts and technology of WSN with the concepts and technology of distributed robotics and multi-agent systems can open new ways to design systems able to provide intelligent services in our homes and working places. We also claim that endowing these systems with learning capabilities can greatly increase their viability and acceptability, by simplifying design, customization and adaptation to changing user needs. To support these claims, we illustrate our architecture for an adaptive robotic ecology, named RUBICON, consisting of a network of sensors, effectors and mobile robots