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AdaptPack studio translator: translating offline programming to real palletizing robots
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Purpose
This paper aims to propose a translation library capable of generating robots proprietary code after their offline programming has been performed in a software application, named AdaptPack Studio, running over a robot simulation and offline programming software package.
Design/methodology/approach
The translation library, named AdaptPack Studio Translator, is capable to generate proprietary code for the Asea Brown Boveri, FANUC, Keller und Knappich Augsburg and Yaskawa Motoman robot brands, after their offline programming has been performed in the AdaptPack Studio application.
Findings
Simulation and real tests were performed showing an improvement in the creation, operation, modularity and flexibility of new robotic palletizing systems. In particular, it was verified that the time needed to perform these tasks significantly decreased.
Practical implications
The design and setup of robotics palletizing systems are facilitated by an intuitive offline programming system and by a simple export command to the real robot, independent of its brand. In this way, industrial solutions can be developed faster, in this way, making companies more competitive.
Originality/value
The effort to build a robotic palletizing system is reduced by an intuitive offline programming system (AdaptPack Studio) and the capability to export command to the real robot using the AdaptPack Studio Translator. As a result, companies have an increase in competitiveness with a fast design framework. Furthermore, and to the best of the author’s knowledge, there is also no scientific publication formalizing and describing how to build the translators for industrial robot simulation and offline programming software packages, being this a pioneer publication in this area.
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Effectiveness of Sentinel-2 in Multi-Temporal Post-Fire Monitoring When Compared with UAV Imagery
Get PDFUnmanned aerial vehicles (UAVs) have become popular in recent years and are now used in a wide variety of applications. This is the logical result of certain technological developments that occurred over the last two decades, allowing UAVs to be equipped with different types of sensors that can provide high-resolution data at relatively low prices. However, despite the success and extraordinary results achieved by the use of UAVs, traditional remote sensing platforms such as satellites continue to develop as well. Nowadays, satellites use sophisticated sensors providing data with increasingly improving spatial, temporal and radiometric resolutions. This is the case for the Sentinel-2 observation mission from the Copernicus Programme, which systematically acquires optical imagery at high spatial resolutions, with a revisiting period of five days. It therefore makes sense to think that, in some applications, satellite data may be used instead of UAV data, with all the associated benefits (extended coverage without the need to visit the area). In this study, Sentinel-2 time series data performances were evaluated in comparison with high-resolution UAV-based data, in an area affected by a fire, in 2017. Given the 10-m resolution of Sentinel-2 images, different spatial resolutions of the UAV-based data (0.25, 5 and 10 m) were used and compared to determine their similarities. The achieved results demonstrate the effectiveness of satellite data for post-fire monitoring, even at a local scale, as more cost-effective than UAV data. The Sentinel-2 results present a similar behavior to the UAV-based data for assessing burned areas.</jats:p
Supporting the analysis of safety critical user interfaces: An Exploration of Three Formal Tools
Get PDFUse error due to user interface design defects is a major concern in many safety critical domains, for example avionics and health care. Early detection of latent user interface problems can be facilitated by user-centered design methods that integrate formal verification technologies. This article considers the role that formal verification technologies can play in the context of user-centered design by considering the following three existing tools: CIRCUS, PVSio-web, and IVY. These tools have been developed to support the model based analysis of critical user interfaces. They have their foundations in existing formal verification technologies, but each of them is focused towards particular issues relating to user interface design. The article explores the different phases of the user-centered design process and the extent to which each of these tools supports these phases. Criteria are developed for assessing their role at each stage of the design process. The results of the evaluation provide guidance to developers to help choose the most appropriate tool based on their analysis needs while at the same time setting challenges for future developments. © 2020 ACM
A component-based framework for certification of components in a cloud of HPC services
Get PDFHPC Shelf is a proposal of a cloud computing platform to provide component-oriented services for High Performance Computing (HPC) applications. This paper presents a Verification-as-a-Service (VaaS) framework for component certification on HPC Shelf. Certification is aimed at providing higher confidence that components of parallel computing systems of HPC Shelf behave as expected according to one or more requirements expressed in their contracts. To this end, new abstractions are introduced, starting with certifier components. They are designed to inspect other components and verify them for different types of functional, non-functional and behavioral requirements. The certification framework is naturally based on parallel computing techniques to speed up verification tasks. © 201
System Identification of Just Walk: Using Matchable-Observable Linear Parametrizations
Get PDFSystem identification approaches have been used to design an experiment, generate data, and estimate dynamical system models for Just Walk, a behavioral intervention intended to increase physical activity in sedentary adults. The estimated models serve a number of important purposes, such as understanding the factors that influence behavior and as the basis for using control systems as decision algorithms in optimized interventions. A class of identification algorithms known as matchable-observable linear identification has been reformulated and adapted to estimate linear time-invariant models from data obtained from this intervention. The experimental design, estimation algorithms, and validation procedures are described, with the best models estimated from data corresponding to an individual intervention participant. The results provide insights into the individual and the intervention, which can be used to improve the design of future studies. IEE
Towards a holistic semantic support for context-aware network monitoring: An ontology-based approach
Get PDFMonitoring current communication networks and services is an increasingly complex task as a result of a growth in the number and variety of components involved. Moreover, different perspectives on network monitoring and optimisation policies must be considered to meet context-dependent monitoring requirements. To face these demanding expectations, this article proposes a semantic-based approach to support the flexible configuration of context-aware network monitoring, where traffic sampling is used to improve efficiency. Thus, a semantic layer is proposed to provide with a standard and interoperable description of the elements, requirements and relevant features in the monitoring domain. On top of this description, semantic rules are applied to make decisions regarding monitoring and auditing policies in a proactive and context-aware manner. Use cases focusing on traffic accounting and traffic classification as monitoring tasks are also provided, demonstrating the expressiveness of the ontology and the contribution of smart SWRL rules for recommending optimised configuration profiles. © 2020, Springer-Verlag GmbH Austria, part of Springer Nature
Individual Grapevine Analysis in a Multi-Temporal Context Using UAV-Based Multi-Sensor Imagery
Get PDFThe use of unmanned aerial vehicles (UAVs) for remote sensing applications in precision viticulture significantly increased in the last years. UAVs’ capability to acquire high spatiotemporal resolution and georeferenced imagery from different sensors make them a powerful tool for a better understanding of vineyard spatial and multitemporal heterogeneity, allowing the estimation of parameters directly impacting plants’ health status. In this way, the decision support process in precision viticulture can be greatly improved. However, despite the proliferation of these innovative technologies in viticulture, most of the published studies rely only on data from a single sensor in order to achieve a specific goal and/or in a single/small period of the vineyard development. In order to address these limitations and fully exploit the advantages offered by the use of UAVs, this study explores the multi-temporal analysis of vineyard plots at a grapevine scale using different imagery sensors. Individual grapevine detection enables the estimation of biophysical and geometrical parameters, as well as missing grapevine plants. A validation procedure was carried out in six vineyard plots focusing on the detected number of grapevines and missing grapevines. A high overall agreement was obtained concerning the number of grapevines present in each row (99.8%), as well as in the individual grapevine identification (mean overall accuracy of 97.5%). Aerial surveys were conducted in two vineyard plots at different growth stages, being acquired for RGB, multispectral and thermal imagery. Moreover, the extracted individual grapevine parameters enabled us to assess the vineyard variability in a given epoch and to monitor its multi-temporal evolution. This type of analysis is critical for precision viticulture, constituting as a tool to significantly support the decision-making process.</jats:p
Efficient procedures for the weighted squared tardiness permutation flowshop scheduling problem
Get PDFThis paper addresses a permutation flowshop scheduling problem, with the objective of minimizing total weighted squared tardiness. The focus is on providing efficient procedures that can quickly solve medium or even large instances. Within this context, we first present multiple dispatching heuristics. These include general rules suited to various due date-related environments, heuristics developed for the problem with a linear objective function, and procedures that are suitably adapted to take the squared objective into account. Then, we describe several improvement procedures, which use one or more of three techniques. These procedures are used to improve the solution obtained by the best dispatching rule. Computational results show that the quadratic rules greatly outperform the linear counterparts, and that one of the quadratic rules is the overall best performing dispatching heuristic. The computational tests also show that all procedures significantly improve upon the initial solution. The non-dominated procedures, when considering both solution quality and runtime, are identified. The best dispatching rule, and two of the non-dominated improvement procedures, are quite efficient, and can be applied to even very large-sized problems. The remaining non-dominated improvement method can provide somewhat higher quality solutions, but it may need excessive time for extremely large instances