Robotics Middleware: A Comprehensive Literature Survey and Attribute-Based Bibliography

Autonomous robots are complex systems that require the interaction between numerous heterogeneous components (software and hardware). Because of the increase in complexity of robotic applications and the diverse range of hardware, robotic middleware is designed to manage the complexity and heterogeneity of the hardware and applications, promote the integration of new technologies, simplify software design, hide the complexity of low-level communication and the sensor heterogeneity of the sensors, improve software quality, reuse robotic software infrastructure across multiple research efforts, and to reduce production costs. This paper presents a literature survey and attribute-based bibliography of the current state of the art in robotic middleware design. The main aim of the survey is to assist robotic middleware researchers in evaluating the strengths and weaknesses of current approaches and their appropriateness for their applications. Furthermore, we provide a comprehensive set of appropriate bibliographic references that are classified based on middleware attributes.http://dx.doi.org/10.1155/2012/95901

EUD-MARS: End-User Development of Model-Driven Adaptive Robotics Software Systems

Empowering end-users to program robots is becoming more significant. Introducing software engineering principles into end-user programming could improve the quality of the developed software applications. For example, model-driven development improves technology independence and adaptive systems act upon changes in their context of use. However, end-users need to apply such principles in a non-daunting manner and without incurring a steep learning curve. This paper presents EUD-MARS that aims to provide end-users with a simple approach for developing model-driven adaptive robotics software. End-users include people like hobbyists and students who are not professional programmers but are interested in programming robots. EUD-MARS supports robots like hobby drones and educational humanoids that are available for end-users. It offers a tool for software developers and another one for end-users. We evaluated EUD-MARS from three perspectives. First, we used EUD-MARS to program different types of robots and assessed its visual programming language against existing design principles. Second, we asked software developers to use EUD-MARS to configure robots and obtained their feedback on strengths and points for improvement. Third, we observed how end-users explain and develop EUD-MARS programs, and obtained their feedback mainly on understandability, ease of programming, and desirability. These evaluations yielded positive indications of EUD-MARS

ハンドとひもの関係に基づくひも結び作業の実行

電気通信大学201

Middleware control systems design and analysis using message interpreted Petri Nets (MIPN)

Many distributed frameworks use a message-oriented middleware to interchange information among several independent distributed modules. Those modules make up complex systems implementing basic actions and reporting events about their state. This paper introduces the Message Interpreted Petri Net (MIPN) model to design, analyze, and execute the central control of these middleware systems. The MIPN is a new Petri net extension that adds message-based high-level information communications and hierarchic capabilities. It also contributes to the definition and study of new properties such as terminability for the hierarchy-wide analysis of a system. Special attention is given to the analyzability of the model. Useful relations between the individual properties of each MIPN and the global properties of a hierarchic MIPNs system are extracted through a mathematical analysis of the model. The goal is to analyze each net separately and then build up the properties of the whole system. This results in a great aid for the programmer and optimizes the development process. This paper also shows the actual integration of this new MIPN model in different robot control frameworks to design, analyze, execute, monitor, log, and debug tasks in such heterogeneous systems. Finally, some applications created with this framework in the fields of robotics, autonomous vehicles, and logistics are also presentedMinisterio de Ciencia e Innovación | Ref. EXP00139978CER-2021100

Methodology for Task Development in Humanoid Robots Using ArmarX Framework

In this bachelor thesis, a guide on how to take the first steps in the ArmarX framework. This software provides a complete robot development environment. In particular, how to use the humanoid robot TEO of the investigation group RoboticsLab from University Carlos III in this software. In order to complete this objective, the several stages were followed. First, a initial research about the software ArmarX was performed, learning its main features and how to use this tool. Then, the 3D model of TEO was adapted to be used in this environment. When it was complete, a task was created and adapted to the robot. Finally, it was exported to the real-world TEO and the results were compared. During this process, an extensive documentation was performed, in order to pursue the objective previously mentioned, the formulation a guide for future researchers interested in ArmarX.Ingeniería en Tecnologías Industriale

DIANNE: a modular framework for designing, training and deploying deep neural networks on heterogeneous distributed infrastructure

Deep learning has shown tremendous results on various machine learning tasks, but the nature of the problems being tackled and the size of state-of-the-art deep neural networks often require training and deploying models on distributed infrastructure. DIANNE is a modular framework designed for dynamic (re)distribution of deep learning models and procedures. Besides providing elementary network building blocks as well as various training and evaluation routines, DIANNE focuses on dynamic deployment on heterogeneous distributed infrastructure, abstraction of Internet of Things (loT) sensors, integration with external systems and graphical user interfaces to build and deploy networks, while retaining the performance of similar deep learning frameworks. In this paper the DIANNE framework is proposed as an all-in-one solution for deep learning, enabling data and model parallelism though a modular design, offloading to local compute power, and the ability to abstract between simulation and real environment. (C) 2018 Elsevier Inc. All rights reserved