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Drenjanac D.,The Telecommunications Research Center Vienna | Tomic S.,The Telecommunications Research Center Vienna | Aguera J.,University of Cordoba, Spain | Perez-Ruiz M.,University of Seville
Sensors (Switzerland) | Year: 2014

In the new agricultural scenarios, the interaction between autonomous tractors and a human operator is important when they jointly perform a task. Obtaining and exchanging accurate localization information between autonomous tractors and the human operator, working as a team, is a critical to maintaining safety, synchronization, and efficiency during the execution of a mission. An advanced localization system for both entities involved in the joint work, i.e., the autonomous tractors and the human operator, provides a basis for meeting the task requirements. In this paper, different localization techniques for a human operator and an autonomous tractor in a field environment were tested. First, we compared the localization performances of two global navigation satellite systems’ (GNSS) receivers carried by the human operator: (1) an internal GNSS receiver built into a handheld device; and (2) an external DGNSS receiver with centimeter-level accuracy. To investigate autonomous tractor localization, a real-time kinematic (RTK)-based localization system installed on autonomous tractor developed for agricultural applications was evaluated. Finally, a hybrid localization approach, which combines distance estimates obtained using a wireless scheme with the position of an autonomous tractor obtained using an RTK-GNSS system, is proposed. The hybrid solution is intended for user localization in unstructured environments in which the GNSS signal is obstructed. The hybrid localization approach has two components: (1) a localization algorithm based on the received signal strength indication (RSSI) from the wireless environment; and (2) the acquisition of the tractor RTK coordinates when the human operator is near the tractor. In five RSSI tests, the best result achieved was an average localization error of 4 m. In tests of real-time position correction between rows, RMS error of 2.4 cm demonstrated that the passes were straight, as was desired for the autonomous tractor. From these preliminary results, future work will address the use of autonomous tractor localization in the hybrid localization approach. © 2014 by the authors; licensee MDPI, Basel, Switzerland. Source


Schuster A.,Vienna University of Technology | Bessler S.,The Telecommunications Research Center Vienna | Gronbaek J.,The Telecommunications Research Center Vienna
Elektrotechnik und Informationstechnik | Year: 2012

In this work we describe the brokerage function between electric vehicle users searching for a charging spot and the charging stations providing the charging service. We propose a routing service for locating and reserving charging spots. Furthermore, we extend the search for charging stations to the case where the trip from the charging station to destination is made using public transportation. Further contributions address the load balancing functionality at the charging station and at the low voltage grid level. In order to realize the latter, we argue for the introduction of a bidirectional interface between the charging station and the DSO, and show how available power for charging stations can be dynamically calculated. Finally, we analyze in detail different charge control concepts for grid component protection. © Springer-Verlag 2012. Source


Drenjanac D.,The Telecommunications Research Center Vienna | Klausner L.,Vienna University of Technology | Kuhn E.,Vienna University of Technology | Tomic S.D.K.,The Telecommunications Research Center Vienna
Communications in Computer and Information Science | Year: 2013

Task allocation is a fundamental problem in multi-robot systems where heterogeneous robots cooperate to perform a complex mission. A general requirement in a task allocation algorithm is to find an optimal set of robots to execute a certain task. This paper describes how coordination capabilities of the space-based middleware are extended with the semantic model of robot capabilities to improve the process of selection in terms of flexibility, scalability and reduced communication overhead during task allocation. We developed a framework that translates resources into a newly defined semantic model and performs automatic reasoning to assist the task allocation. We conducted performance tests in a specific precision agriculture use case based on the robotic fleet for weed control elaborated within European Project RHEA-Robot Fleets for Highly Effective Agriculture and Forestry Management. © Springer International Publishing Switzerland 2013. Source


Tomic S.D.K.,The Telecommunications Research Center Vienna | Fensel A.,The Telecommunications Research Center Vienna | Aschauer C.,University of Natural Resources and Life Sciences, Vienna | Schulmeister K.G.,University of Natural Resources and Life Sciences, Vienna | And 4 more authors.
Communications in Computer and Information Science | Year: 2013

Today, users involved in agricultural production processes increasingly rely on advanced agricultural machines and specialized applications utilizing the latest advances in information and communication technology (ICT). Robots and machines host numerous specialized sensors and measurement devices and generate large amounts of data that combined with data coming from external sources, could provide a basis for better process understanding and process optimization. One serious roadblock to this vision is a lack of interoperability between the equipment of different vendors; another pitfall of current solutions is that the process knowledge is not modelled in a standardized machine readable form. On the other hand, such process model can be flexibly used to support process-specific integration of machines, and enable context-sensitive automatic process optimization. This paper presents an approach and preliminary results regarding architecture for adaptive optimization of agricultural processes via open interfaces, linked data and semantic services that is being developed within the project agriOpenLink; its goal is to provide a novel methodology and tools for semantic proces orchestraion and dynamic context-based adaptation, significantly reducing the effort needed to create new ICT-controlled agricultural applications involving machines and users. © Springer International Publishing Switzerland 2013. Source


Girtelschmid S.,Johannes Kepler University | Steinbauer M.,Johannes Kepler University | Kumar V.,The Telecommunications Research Center Vienna | Fensel A.,The Telecommunications Research Center Vienna | Kotsis G.,Johannes Kepler University
International Journal of Pervasive Computing and Communications | Year: 2014

Purpose – The purpose of this article is to propose and evaluate a novel system architecture for Smart City applications which uses ontology reasoning and a distributed stream processing framework on the cloud. In the domain of Smart City, often methodologies of semantic modeling and automated inference are applied. However, semantic models often face performance problems when applied in large scale. Design/methodology/approach – The problem domain is addressed by using methods from Big Data processing in combination with semantic models. The architecture is designed in a way that for the Smart City model still traditional semantic models and rule engines can be used. However, sensor data occurring at such Smart Cities are pre-processed by a Big Data streaming platform to lower the workload to be processed by the rule engine. Findings – By creating a real-world implementation of the proposed architecture and running simulations of Smart Cities of different sizes, on top of this implementation, the authors found that the combination of Big Data streaming platforms with semantic reasoning is a valid approach to the problem. Research limitations/implications – In this article, real-world sensor data from only two buildings were extrapolated for the simulations. Obviously, real-world scenarios will have a more complex set of sensor input values, which needs to be addressed in future work. Originality/value – The simulations show that merely using a streaming platform as a buffer for sensor input values already increases the sensor data throughput and that by applying intelligent filtering in the streaming platform, the actual number of rule executions can be limited to a minimum. © Emerald Group Publishing Limited Source

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