Transfer Learning for Improving Model Predictions in Highly Configurable Software

Modern software systems are built to be used in dynamic environments using configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and we end up learning an exponentially large configuration space. Naturally, this does not scale when relying on real measurements in the actual changing environment. We propose a different solution: Instead of taking the measurements from the real system, we learn the model using samples from other sources, such as simulators that approximate performance of the real system at low cost. We define a cost model that transform the traditional view of model learning into a multi-objective problem that not only takes into account model accuracy but also measurements effort as well. We evaluate our cost-aware transfer learning solution using real-world configurable software including (i) a robotic system, (ii) 3 different stream processing applications, and (iii) a NoSQL database system. The experimental results demonstrate that our approach can achieve (a) a high prediction accuracy, as well as (b) a high model reliability.Comment: To be published in the proceedings of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS'17

A framework for fault detection and diagnostics of articulated collaborative robots based on hybrid series modelling of Artificial Intelligence algorithms

Smart factories build on cyber-physical systems as one of the most promising technological concepts. Within smart factories, condition-based and predictive maintenance are key solutions to improve competitiveness by reducing downtimes and increasing the overall equipment effectiveness. Besides, the growing interest towards operation flexibility has pushed companies to introduce novel solutions on the shop floor, leading to install cobots for advanced human-machine collaboration. Despite their reliability, also cobots are subjected to degradation and functional failures may influence their operation, leading to anomalous trajectories. In this context, the literature shows gaps in what concerns a systematic adoption of condition-based and predictive maintenance to monitor and predict the health state of cobots to finally assure their expected performance. This work proposes an approach that leverages on a framework for fault detection and diagnostics of cobots inspired by the Prognostics and Health Management process as a guideline. The goal is to habilitate first-level maintenance, which aims at informing the operator about anomalous trajectories. The framework is enabled by a modular structure consisting of hybrid series modelling of unsupervised Artificial Intelligence algorithms, and it is assessed by inducing three functional failures in a 7-axis collaborative robot used for pick and place operations. The framework demonstrates the capability to accommodate and handle different trajectories while notifying the unhealthy state of cobots. Thanks to its structure, the framework is open to testing and comparing more algorithms in future research to identify the best-in-class in each of the proposed steps given the operational context on the shop floor

Smart working technologies in industry 4.0 : contributions to different manufacturing activities and workers’ skills

A Indústria 4.0 é considerada a quarta revolução industrial porque utiliza uma ampla integração de tecnologias de informação e de operação na fabricação industrial. Apesar dessa perspectiva tecnológica, diversos estudos vêm evidenciando a importância de considerar o fator humano para o desenvolvimento de um sistema de manufatura inteligente. Nesse sentido, a dimensão denominada como Smart Working precisa ser melhor investigada, uma vez que entender como as tecnologias afetam os trabalhadores e as habilidades desses são cruciais para o bom desempenho das fábricas. Em razão disso, o objetivo desta dissertação foi entender como as Smart Working Technologies (SWT) podem contribuir para as atividades e as habilidades dos trabalhadores da manufatura. Para tanto, primeiramente foi realizada uma análise abrangente da literatura para identificar as SWT e seus impactos nas capacidades dos trabalhadores em suas atividades de manufatura. Deste modo, foram analisados 80 artigos que relacionam as SWT em oito atividades de manufatura. Posteriormente, foi selecionada uma das SWT mais relevantes conforme a literatura, os robôs colaborativos, para identificar os efeitos das tecnologias nas habilidades dos trabalhadores. Deste modo, foram analisados 138 casos de aplicação reportados por uma das empresas fornecedoras líderes mundiais, bem como três entrevistas com empresas adotantes da tecnologia. Os resultados apontam que existem 15 SWT que podem ser implementadas nas atividades de manufatura e relacionadas às capacidades dos trabalhadores. Além disso, os resultados também apontam que podem existir quatro efeitos das SWT nas habilidades dos trabalhadores. Estes achados demonstram que de acordo com a estratégia da empresa uma SWT pode impactar de diferentes formas os trabalhadores.Industry 4.0 is considered the fourth industrial revolution because it uses a broad integration of information and operating technologies in industrial manufacturing. Despite this technological perspective, several studies have highlighted the importance of considering the human factor to develop a smart manufacturing system. In this sense, the Smart Working dimension needs to be further investigated since understanding how technologies affect workers and their skills are crucial for factories' good performance. Therefore, the objective of this dissertation was to understand how Smart Working Technologies (SWT) can contribute to the activities and skills of manufacturing workers. To this end, firstly a systematic literature review was carried out to identify SWTs and their impacts on workers' capabilities in their manufacturing activities. Thus, 80 articles relating to SWT in eight manufacturing activities were analyzed. Subsequently, one of the most relevant SWTs according to the literature, collaborative robots, was selected to identify the effects of technologies on workers' skills. In this way, 138 application cases reported by one of the world's leading supplier companies were analyzed, as well as three interviews with companies that adopted the technology. The results show that there are 15 SWT that can be implemented in manufacturing activities and related to workers' capabilities. In addition, the results also point out that there may be four effects of SWT on workers' skills. According to the company's strategy, these findings demonstrate that an SWT can impact workers in different ways

Toward Controllable Hydraulic Coupling of Joints in a Wearable Robot

In this paper, we develop theoretical foundations for a new class of rehabilitation robot: body powered devices that route power between a user’s joints. By harvesting power from a healthy joint to assist an impaired joint, novel bimanual and self-assist therapies are enabled. This approach complements existing robotic therapies aimed at promoting recovery of motor function after neurological injury. We employ hydraulic transmissions for routing power, or equivalently for coupling the motions of a user’s joints. Fluid power routed through flexible tubing imposes constraints within a limb or between homologous joints across the body. Variable transmissions allow constraints to be steered on the fly, and simple valve switching realizes free space and locked motion. We examine two methods for realizing variable hydraulic transmissions: using valves to switch among redundant cylinders (digital hydraulics) or using an intervening electromechanical link. For both methods, we present a rigorous mathematical framework for describing and controlling the resulting constraints. Theoretical developments are supported by experiments using a prototype fluid-power exoskeleton

FABRIC: A Framework for the Design and Evaluation of Collaborative Robots with Extended Human Adaptation

A limitation for collaborative robots (cobots) is their lack of ability to adapt to human partners, who typically exhibit an immense diversity of behaviors. We present an autonomous framework as a cobot's real-time decision-making mechanism to anticipate a variety of human characteristics and behaviors, including human errors, toward a personalized collaboration. Our framework handles such behaviors in two levels: 1) short-term human behaviors are adapted through our novel Anticipatory Partially Observable Markov Decision Process (A-POMDP) models, covering a human's changing intent (motivation), availability, and capability; 2) long-term changing human characteristics are adapted by our novel Adaptive Bayesian Policy Selection (ABPS) mechanism that selects a short-term decision model, e.g., an A-POMDP, according to an estimate of a human's workplace characteristics, such as her expertise and collaboration preferences. To design and evaluate our framework over a diversity of human behaviors, we propose a pipeline where we first train and rigorously test the framework in simulation over novel human models. Then, we deploy and evaluate it on our novel physical experiment setup that induces cognitive load on humans to observe their dynamic behaviors, including their mistakes, and their changing characteristics such as their expertise. We conduct user studies and show that our framework effectively collaborates non-stop for hours and adapts to various changing human behaviors and characteristics in real-time. That increases the efficiency and naturalness of the collaboration with a higher perceived collaboration, positive teammate traits, and human trust. We believe that such an extended human adaptation is key to the long-term use of cobots.Comment: The article is in review for publication in International Journal of Robotics Researc

A novel integrated industrial approach with cobots in the age of industry 4.0 through conversational interaction and computer vision

From robots that replace workers to robots that serve as helpful colleagues, the field of robotic automation is experiencing a new trend that represents a huge challenge for component manufacturers. The contribution starts from an innovative vision that sees an ever closer collaboration between Cobot, able to do a specific physical job with precision, the AI world, able to analyze information and support the decision-making process, and the man able to have a strategic vision of the future

Challenges in the Safety-Security Co-Assurance of Collaborative Industrial Robots

The coordinated assurance of interrelated critical properties, such as system safety and cyber-security, is one of the toughest challenges in critical systems engineering. In this chapter, we summarise approaches to the coordinated assurance of safety and security. Then, we highlight the state of the art and recent challenges in human-robot collaboration in manufacturing both from a safety and security perspective. We conclude with a list of procedural and technological issues to be tackled in the coordinated assurance of collaborative industrial robots.Comment: 23 pages, 4 tables, 1 figur