Technical Maturity for Industrial Deployment of Robot Demonstrators

Any technical development done in the context of agile manufacturing has limited benefit if it's not industrially utilized. This requires maturing the developed technologies to a point that they are robust enough to provide a productivity boost, while at the same time adhering to the relevant industrial standards. In this paper we present the various stages in which different robot demonstrators were able to achieve the required technical maturity for industrial deployment. We present the context about the importance of developing technologies that facilitate agile manufacturing followed by the gap between the state of the art and the state of the practice, due to which many promising technologies do not end up being deployed in the industry as they were not subjected to maturity actions required for the transition. We present the journey of four industrial demonstrators that bridged this gap. Furthermore, we provide the assessment methods to ascertain the iterative developmental steps, and present a generic approach to improve the technological readiness.acceptedVersionPeer reviewe

Digital innovation hubs for robotics - TRINITY approach for distributing knowledge via modular use case demonstrations

Robots are no longer stand-alone systems on the factory floor. The demand for industrial robots (market) is anticipated to be growing to 65 billion euros by the year 2023. Within all areas of robotics, the demand for collaborative and more flexible systems is rising as well. The level of desired collaboration and increased flexibility will only be reached if the systems are developed as a whole, e.g. perception, reasoning and physical manipulation. The rising need for collaborative robots in the automation industry is acting as a driver for this market and is expected to serve as a market opportunity for future growth. However, at the same time especially smaller companies have difficulties to formulate a concrete vision and strategies for the uptake of robotics, finding skilled workforce to develop and deploy the robot systems and/or work in the manufacturing industry. A number of Digital Innovation Hubs (DIHs) have been developed to enhance the knowledge and technology transfer from laboratories to factory floors, mitigating the skills gap and supporting the formulation of innovation ecosystems with the specific focus on small and medium-sized companies around Europe. The main aim of this paper is to introduce the concept and approach taken in H2020 TRINITY-project that aims to develop a Robotics Innovation Hub focused on Agile Production. The paper will introduce the concept and technical approach of the project, and discusses the preliminary results, challenges and opportunities of these kind of DIHs.publishedVersionPeer reviewe

Enabling Flexibility in Manufacturing by Integrating Shopfloor and Process Perception for Mobile Robot Workers

Robotic flexibility in industry is becoming more and more relevant nowadays, especially with the rise of the Industry 4.0 concept. This paper presents a smart execution control framework for enabling the autonomous operation of flexible mobile robot workers. These robot resources are able to autonomously navigate the shopfloor, undertaking multiple operations while acting as assistants to human operators. To enable this autonomous behavior, the proposed framework integrates robot perception functions for the real-time shopfloor and process understanding while orchestrating the process execution. A Digital World Model is deployed synthesizing the sensor data coming from multiple 2D and 3D sensors from the shopfloor. This model is consumed for the perception functions enabling the real-time shopfloor and process perception by the robot workers. This smart control system has been applied and validated in a case study from the automotive sector

Using Post-Emergence Herbicides in Combination with the Sowing Date to Suppress Sinapis arvensis and Silybum marianum in Durum Wheat

Wild mustard (Sinapis arvensis L.) and milk thistle (Silybum marianum (L.) Gaertn.) are two competitive broad-leaved weeds commonly found in cereals in Europe, while several weed species have developed resistance to the main herbicides that are applied on these crops. Thus, the implementation of integrated weed management (IWM) programs is of great importance. Field experiments were conducted based on a split-plot design with two factors (sowing date and herbicides). Our results showed that the density of wild mustard and milk thistle was higher in the early sowing compared to the late sowing, while the total weed density was up to 75% higher in early sowing. Moreover, the herbicides florasulam + 2.4-D and bromoxynil + 2.4-D exhibited high efficacy (>98%) against milk thistle and wild mustard, while tribenuron-methyl and florasulam + clopyralid provided greater efficacy in the late sowing compared to the early sowing. Among the four herbicides, the lowest dry biomass and grain yield of wheat were observed in tribenuron-methyl and florasulam + clopyralid, while in the weed-infested treatment, the highest values of both parameters were recorded in late sowing. Finally, the results showed that the sowing date is a cultural weed control method that should be implemented in IWM programs, since it can affect both weed density and herbicide efficacy

Assistant: Learning and robust decision support system for agile manufacturing environments

The European project ASSISTANT will provide a set of AI-based digital twins that helps process engineers and production planners to operate collaborative mixed-model assembly lines based on the data collected from IoT devices and external data sources. Such a tool will help planners to design the assembly line, plan the production, operate the line, and improve process tuning. In addition, the system monitors the line in real-time, ensures that all required resources are available, and allows fast re-planning when necessary. ASSISTANT aims to make cost-effective decisions while ensuring product quality, safety and wellbeing of the workers, and managing the various sources of uncertainties. The resulting digital twin systems will be data-driven, agile, autonomous, collaborative and explainable, safe but reactive

Open-Digital-Industrial and Networking pilot lines using modular components for scalable production - ODIN project approach

While robots have very well proven their flexibility and efficiency in mass production and are recognized as the production resource of the future, their adoption in lower volume, diverse environment is heavily constrained. The main reason for this is the high integration and deployment complexity that overshadows the performance benefits of this technology. This paper presents the vision of ODIN European funded project which is to strengthen the EU production companies' trust in utilizing advanced robotics, by demonstrating that novel robot-based production systems are not only technically feasible, but also efficient and sustainable for immediate introduction at the shopfloor. To achieve that, ODIN brings together, by means of hardware and software, the latest technological advancements in the fields of a) collaborating robots and human robot collaborative workplaces, b) autonomous robotics and AI based task planning, c) mobile robots and reconfigurable tooling, d) Digital Twins and Virtual Commissioning and e) Service Oriented Robotics Integration and Communication Architectures. ODIN will provide a systematic approach for integrating these technologies under modular and reconfigurable large-scale robotic pilots. The performance of these robotic pilots will be tested and validated in three case studies, from the automotive, the white goods and the aeronautics industry.publishedVersionPeer reviewe

Evidence for secondary-variant genetic burden and non-random distribution across biological modules in a recessive ciliopathy

© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc. The influence of genetic background on driver mutations is well established; however, the mechanisms by which the background interacts with Mendelian loci remain unclear. We performed a systematic secondary-variant burden analysis of two independent cohorts of patients with Bardet–Biedl syndrome (BBS) with known recessive biallelic pathogenic mutations in one of 17 BBS genes for each individual. We observed a significant enrichment of trans-acting rare nonsynonymous secondary variants in patients with BBS compared with either population controls or a cohort of individuals with a non-BBS diagnosis and recessive variants in the same gene set. Strikingly, we found a significant over-representation of secondary alleles in chaperonin-encoding genes—a finding corroborated by the observation of epistatic interactions involving this complex in vivo. These data indicate a complex genetic architecture for BBS that informs the biological properties of disease modules and presents a model for secondary-variant burden analysis in recessive disorders