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Im Zuge der fortschreitenden Globalisierung und zunehmenden Digitalisierung der Arbeitswelt, ergeben sich gerade für kleine und mittelständische Unternehmen Herausforderungen im Bereich der Produktion und insbesondere in der Montage. Steigende Komplexität der Produkte, kürzer werdende Produktlebenszyklen bei kleinen Losgrößen mit hoher Variantenvielfalt und großem Wettbewerbsdruck zwingen Unternehmen bereits vorhandene Montagestrategien zu überarbeiten. Gerade bei komplexeren Baugruppen ist eine Hochautomatisierung der Montage in der Serienfertigung aufgrund der Produktstruktur nur schwer realisierbar und oftmals nicht wirtschaftlich. In der industriellen Produktion ist ein klarer Trend von der Massenproduktion hin zur »Massenspezialanfertigung« zu erkennen. Die Leistungsfähigkeit eines Industriebetriebes hängt entscheidend von den angewandten Produktionsverfahren, den eingesetzten Produktionsmitteln und der eingeführten Produktionsorganisation ab. Aber auch das Zusammenspiel von Mensch, Organisation und Technik trägt dazu bei Potenziale für den Erfolg eines Unternehmens auszuschöpfen. Aufgrund sich ständig ändernder Marktbedingungen und der kostengünstigen Herstellung von Produkten in Niedriglohnländern bei gleichzeitig steigendem Qualitätsniveau müssen Produktionsstrukturen ständig neu überdacht und weiterentwickelt werden. Die Montage ist im Vergleich zur klassischen Fertigung (wie z.B. Drehen, Fräsen oder Schleifen) ein eher niedrig automatisierter Bereich der industriellen Produktion, indem allerdings eine hohe Wertschöpfung am Produkt stattfindet. Die manuelle Montage ist daher auch zukünftig ein wichtiger Bestandteil der industriellen Produktion. Um am Markt bestehen zu können, müssen Unternehmen nach Möglichkeiten suchen, um manuelle Arbeit produktiver und damit kosteneffizienter zu gestalten ohne dabei Abstriche bei der Qualität hinzunehmen. Zur Unterstützung manueller Tätigkeiten sind in den vergangenen Jahren verschiedene Innovationen in den Fokus der Unternehmen gerückt. Das Ziel muss es sein, den (Montage-)Standort Deutschland durch innovative Konzepte wie kollaborative Mensch-Roboter-Arbeitsplätze zu sichern.
Das IDEE ist mit vier Forschungsprofessoren besetzt, die durch eine Lehrentlastung Freiräume für den Auf- und Ausbau von interdisziplinären Forschungsaktivitäten nutzen können. Das Center Robotik (CERI) ist eines der Aufgabengebiete des IDEEs mit den beiden Schwerpunkten »Robotergestützte Arbeitsplätze der Zukunft« sowie »Intralogistik in der Smart Factory«.
Dieser Artikel beschäftigt sich mit dem zweiten Schwerpunkt. Anhand der CERI Aktivitäten im Bereich der fahrerlosen Transportsysteme wird aufgezeigt, wie der »Dreiklang« Forschung, Lehre und Transfer in der Praxis umgesetzt wird und die drei Bestandteile sich gegenseitig befruchten können.
Highly autonomous production cells are a crucial part of manufacturing systems in industry 4.0 and can contribute to a sustainable value-adding process. To realize a high degree of autonomy in production cells with an industrial robot and a machine tool, an experimental approach was carried out to deal with numerous challenges on various automation levels. One crucial aspect is the scheduling problem of tasks for each resource (machine tool, tools, robot, AGV) depending on various data needed for a job-shop scheduling algorithm. The findings show that the necessary data has to be derived from different automation levels in a company: horizontally from ERP to shop-floor, vertically from the order handling department to the maintenance department. Utilizing that data, the contribution provides a cascaded scheduling approach for machine tool jobs as well as CNC and robot tasks for highly autonomous production cells supplied by AGVs.
Humans and robots need to exchange information if the objective is to achieve a task cooperatively. Two questions are considered in this paper: what type of information to communicate, and how to cope with the limited resources of human operators. Decision-theoretic human-robot communication can provide answers to both questions: the type of information is determined by the underlying probabilistic representation, and value-of-information theory helps decide when it is appropriate to query operators for information. A robot navigation task is used to evaluate the system by comparing it to conventional teleoperation. The results of a user study show that the developed system is superior with respect to performance, operator workload, and usability.
This paper presents a framework for performing real-time recursive estimation of landmarks’ visual appearance. Imaging data in its original high dimensional space is probabilistically mapped to a compressed low dimensional space through the definition of likelihood functions. The likelihoods are subsequently fused with prior information using a Bayesian update. This process produces a probabilistic estimate of the low dimensional representation of the landmark visual appearance. The overall filtering provides information complementary to the conventional position estimates which is used to enhance data association. In addition to robotics observations, the filter integrates human observations in the appearance estimates. The appearance tracks as computed by the filter allow landmark classification. The set of labels involved in the classification task is thought of as an observation space where human observations are made by selecting a label. The low dimensional appearance estimates returned by the filter allow for low cost communication in low bandwidth sensor networks. Deployment of the filter in such a network is demonstrated in an outdoor mapping application involving a human operator, a ground and an air vehicle.
This paper considers the modes of interaction between one or several human operators and an active sensor network-a fully decentralized network of sensors some or all of which have actuators and are in that sense active. The primary goal of this study is to investigate the conditions under which the human involvement will not jeopardize scalability of the overall system. Two aspects of human-robot interaction are considered: the ways in which the global view of the system may be conveyed to the operators, and how the operators may influence the behavior of the system during the course of its operation. The results of analysis favor peer-to-peer environment-centric interactions between the operators and the network whereby the humans act as extended sensors and communication nodes of the network itself. Experiments on an indoor active sensor network are described.
This paper considers the problem of building a software architecture for a human-robot team. The objective of the team is to build a multi-attribute map of the world by performing information fusion. A decentralized approach to information fusion is adopted to achieve the system properties of scalability and survivability. Decentralization imposes constraints on the design of the architecture and its implementation. We show how a component-based software engineering approach can address these constraints. The architecture is implemented using Orca - a component-based software framework for robotic systems. Experimental results from a deployed system comprised of an unmanned air vehicle, a ground vehicle, and two human operators are presented. A section on the lessons learned is included which may be applicable to other distributed systems with complex algorithms. We also compare Orca to the player software framework in the context of distributed systems.
This paper describes Orca: an open-source software project which applies Component-Based Software Engineering principles to robotics. The paper focuses on the technical aspects of the framework which set it apart from other similar efforts. Among them are the adaption of a commercial middleware package, minimalist approach to framework design, and a commitment to multi-language multi-platform support. The paper explains our decision to use Ice -- a relatively recent entry in the field of general-purpose middleware packages.
Today's reusable robotics software is provided by several self-contained open-source projects with virtually no software reuse between them. Such partitioning leads to problems with software quantity, quality, ease of evaluation and, ultimately, to poor end user experience. By reviewing several of the projects we observe that all of them contain a mix of three types of software: 1) driver and algorithm implementations, 2) communication middleware, and 3) robotic software framework. We show that more than half of the combined code base contains software which could be highly reusable but only a small fraction of it actually is. We argue that formal separation of the three groups in the existing and future software projects would offer several potential advantages. Availability of framework-independent code would enable community-wide library-based software reuse in addition to the existing framework-wide component- based reuse. Another important benefit is related to evaluation procedures. The three software types are very different and should be evaluated separately, using different criteria. The first two types allow quantitative comparisons which are well documented in the literature. The last one is largely qualitative and, therefore, more subjective. Practically, we recommend today's projects to refactor and eject the driver and algorithm implementation code and focus on the task of software integration. Thin frameworks can be numerous, increasing the diversity of options available to robotics practitioners. Finally, we discuss distribution options for the refactored framework-independent code including the option of creating a Common Robotic Project.
To arrive at a realistic assessment of localization methods in terms of their performance in an industrial environment under various challenging conditions, we provide a benchmark to evaluate algorithms both for individual components as well as multi-sensor systems. For several sensor types, including wheel odometry, RGB cameras, RGB-D cameras, and LIDAR, potential issues were identified. The accuracy of wheel odometry, for example, when there are bumps on the track. For each sensor type, we explicitly chose a track for the benchmark dataset containing situations where the sensor fails to provide adequate measurements. Based on the acquired sensor data, localization can be achieved either using a single sensor information or sensor fusion. To help evaluate the output of associated localization algorithms, we provide a software to evaluate a set of metrics as part of the paper. An example application of the benchmark with state-of-the-art algorithms for each sensor is also provided.
This paper proposes a methodology to measure the effectiveness of a human-robot team as part of an adjustable autonomy system. The effectiveness measure is aimed at determining an appropriate autonomy level prior to the system's deployment. Two competing goals need to be traded off: maximising robot performance while minimising the amount of human input. The relative importance of the two goals depend on the mission priorities and constraints which are taken into account. The proposed methodology is applied to a human-robot communication system developed for task- oriented information exchange. The robot uses a decision- theoretic framework to act autonomously and to decide when to request input from human operators. The latter is achieved by computing the value-of-information an operator is able to provide which is compared to the cost of obtaining the information. For our system, the cost parameter represents the autonomy level to be determined. We demonstrate how an appropriate autonomy level can be found experimentally using a navigation task. In our experiment, the robot navigates through a set of simulated worlds with human input being generated by a software component. The results are used to find appropriate autonomy levels for three example missions and a subsequent user study.
In this paper, we present the application of a non-linear dimensionality reduction technique for the learning and probabilistic classification of hyperspectral image. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. It gives much greater information content per pixel on the image than a normal colour image. This should greatly help with the autonomous identification of natural and manmade objects in unfamiliar terrains for robotic vehicles. However, the large information content of such data makes interpretation of hyperspectral images time-consuming and userintensive. We propose the use of Isomap, a non-linear manifold learning technique combined with Expectation Maximisation in graphical probabilistic models for learning and classification. Isomap is used to find the underlying manifold of the training data. This low dimensional representation of the hyperspectral data facilitates the learning of a Gaussian Mixture Model representation, whose joint probability distributions can be calculated offline. The learnt model is then applied to the hyperspectral image at runtime and data classification can be performed.
This paper presents the design of a probabilistic model of human perception as an integral part of a decentralized data fusion system. The system consists of a team of human operators and robotic platforms, together forming a heterogeneous sensor network. Human operators are regarded as information sources submitting raw observations. The observations are converted into a probabilistic representation suitable for fusion with the system's belief. The conversion is performed by a human sensor model (HSM). The initial HSM is built offline based on an average of multiple human subjects conducting a calibration experiment. Since individual human operators may vary in their performance, an online adaptation of the HSM is required. The network estimate is used for adaptation because the true feature state is unknown at runtime. Results of an outdoor calibration experiment using range and bearing observations are presented. Simulations show the feasibility of efficient online adaptation.
This paper considers the problem of building environment models by fusing information gathered by robotic platforms with human perceptual information. Rich environment models are required in real applications for both autonomous operation of robots and to support human decision making. Hierarchical models are well suited to represent complex environments because they: offer multiple abstractions of the available information to support analysis and decision-making, and permit the incorporation of higher-level human observations. The contributions of this paper are two-fold: (1) development of a probabilistic three-level environment model for distributed information gathering, and (2) experimental demonstration of fully decentralized, cooperative human-robot information gathering using an outdoor sensor network comprised of an unmanned air vehicle, a ground vehicle, and two human operators. Several information exchange patterns are presented which qualitatively demonstrate human-robot information fusion
This paper presents an algorithm for real-time sensor-based motion planning under kinodynamic constraints, in unknown environments. The objective of the trajectory-generation algorithm is to optimise a cost function out to a limited time horizon. The space of control trajectories is searched by expanding a tree using randomised sampling, in a manner similar to an RRT. The algorithm is improved by seeding the tree using the best control trajectory from the previous iteration, and by pruning branches based on a bound to the cost function and the best trajectory found so far. Performance of the algorithm is analysed in simulation. In addition, the algorithm has been implemented on two kinds of vehicles: the Segway RMP and a four-wheel-drive. The algorithm has been used to drive autonomously for a combined total on the order of hundreds of hours.
This paper presents an approach of integrating human operators into a sensor network formed by a heterogeneous team of unmanned air and ground vehicles. Several objectives of human-network interaction are identified. The main focus of this work is on human-to-network information flow, i.e. human operators are regarded as information sources. It is argued that operators should make raw observations which are converted into the sensor network's common representation by a probabilistic model. The concepts are discussed in the context of an outdoor sensor network under development. Human operators contribute geometric feature information in the form of range and bearing observations. Visual feature properties are specified via meaningful class labels. A sensor model, represented as a Bayesian network, translates label observations into the system's representation. The model is also used to classify features as observed by robotic sensors.
This paper gives an overview of component-based software engineering (CBSE), motivates its application to the field of mobile robotics, and proposes a particular component model. CBSE is an approach to system-building that aims to shift the emphasis from programming to composing systems from a mixture of off-the-shelf and custom-built software components. This paper argues that robotics is particularly well-suited for and in need of component-based ideas. Furthermore, now is the right time for their introduction. The paper introduces Orca - an open-source component-based software engineering framework proposed for mobile robotics with an associated repository of free, reusable components for building mobile robotic systems.