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  • 1351.
    Wasik, Zbigniew
    et al.
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Robust color segmentation for the RoboCup domain2002In: Proceedings 16th International Conference on Pattern Recognition, 2002, 2002, p. 651-654Conference paper (Refereed)
    Abstract [en]

    Color segmentation is crucial in robotic applications, such as RoboCup, where the relevant objects can be distinguished by their color. In these applications, real-time performance and robustness are primary concerns. We present a hybrid method for color segmentation based on seeded region growing (SRG) in which the initial seeds are provided by a conservative threshold color segmentation. The key to the robustness of our approach is to use multiple seeds to perform local blob growing, and then merge blobs that belong to the same region. We have implemented our technique on a team of Sony AIBO 4-legged robots, and have successfully tested it in the RoboCup 2001 competition

  • 1352.
    Wasik, Zbigniew
    et al.
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Using hierarchical fuzzy behaviors for manipulation2003In: Proc. of the World Congress of the Int. Fuzzy Systems Association, 2003Conference paper (Refereed)
    Abstract [en]

    Behavior-based systems have become extremely popular in autonomous robotics. These systems are typically used to control robots with few degrees of fredom (DOF), like mobile platforms. We propose a behaviorbased system able to control a complex plant with several DOF. The key to deal with complexity is the use of fuzzy logic techniques to compose simple behaviors into more complex ones. In this paper, we illustrate our approach on a 5DOF real manipulator, on which several tasks are performed using the same set of basic behaviors.

  • 1353.
    Wasik, Zbigniew
    et al.
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Using hierarchical fuzzy behaviors for manipulation2003Conference paper (Refereed)
    Abstract [en]

    Behavior-based systems have become extremely popular in autonomous robotics. These systems are typically used to control robots with few degrees of fredom (DOF), like mobile platforms. We propose a behavior-based system able to control a complex plant with several DOF. The key to deal with complexity is the use of fuzzy logic techniques to compose simple behaviors into more complex ones. In this paper, we illustrate our approach on a 5 DOF real manipulator, on which several tasks are performed using the same set of basic behaviors.

  • 1354. Wearing, Thomas
    et al.
    Dragoni, Nicola
    Örebro University, School of Science and Technology.
    Security and Privacy Issues in Health Monitoring Systems: eCare@Home Case Study2016In: Proceedings of the 1st Workshop on Emerging eHealth through Internet of Things (EHIoT 2016), 2016Conference paper (Refereed)
    Abstract [en]

    Automated systems for monitoring elderly people in their home are becoming more and more common. Indeed, an increasing number of home sensor networks for healthcare can be found in the recent literature, indicating a clear research direction in smart homes for health-care. Although the huge amount of sensitive data these systems deal with and expose to the external world, security and privacy issues are surpris-ingly not taken into consideration. The aim of this paper is to raise some key security and privacy issues that home health monitor systems should face with. The analysis is based on a real world monitoring sensor network for healthcare built in the context of the eCare@Home project.

  • 1355.
    Westholm, Erik
    Örebro University, School of Science and Technology.
    A Simulator Tool for Human Activity Recognition2010Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The goal of this project was to create a simulator that was to produce data for research in the field of activity recognition. The simulator was to simulate a human entity moving around in, and interacting with, a PEIS environment. This simulator ended up being based on The Sims 3, and how this was done is described. The reader is expected to have some experience with programming.

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  • 1356.
    Westlind, Casper
    Örebro University, School of Science and Technology.
    Agentbaserad simulation av innovativt logistiksystem2021Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    This report is about the development and analysis of an agent-based simulation whose purpose is to visualize a logistics system for transportation. This work is assigned by Wadköping Logistik AB with an idea on a logistics system that uses an innovative auction-based method that matches customers and trucks with each other with the expectation that route planning will be more optimized.This system has excluded the service provider who generally acts as a third party in a logistics system that provides instructions to carriers on how to handle the transportation. Instead, a direct connection is established between carrier and customer. The behavior of transport vehicles in the system has been visualized in the simulation and important factors that make up a good logistics system have been investigated such as cost and wait time between order and delivery but also general distribution of vehicles on the map and how it could be improved. The map in question has a graph structure that represents an area in Örebro County, which is the area in which Wadköping Logistik AB is operating.

  • 1357.
    Wide, Peter
    et al.
    Linköping University, Linköping, Sweden.
    Driankov, Dimiter
    Linköping University, Linköping, Sweden.
    A fuzzy approach to multi-sensor data fusion for quality profile classification1996In: M'96: IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems, New York, USA: IEEE conference proceedings, 1996, p. 215-221Conference paper (Refereed)
    Abstract [en]

    In the present paper a multi-sensor system is considered wlicre the sensors comprising it utilize the principles of huinan olfactory sensing and the processing of the sensor iiicasurcnients is done by a fuzzy sensor fusion technique. 'l'hc enipliasis of the paper is on the fuzzy fusion technique used for the classification of the numerical measurements of a quality characteristic in different fuzzy quality profiles.

  • 1358.
    Wide, Peter
    et al.
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Bothe, Hans-Heinrich
    Environmental exploration: an autonomous sensory systems approach1999In: IEEE Instrumentation & Measurement Magazine, ISSN 1094-6969, E-ISSN 1941-0123, Vol. 2, no 3, p. 28-32Article in journal (Refereed)
    Abstract [en]

    The purpose of this article is to demonstrate new paradigms in the analysis and design of virtual instrumentation in autonomous sensor systems. By autonomous sensor systems we mean mobile as well as immobile systems that employ a vast array of sensors to analyze or influence dynamic and uncertain external changes. These systems must perform operations in real time, in both expected and unexpected situations, using only limited human intervention. An autonomous sensor system can be used to collect data about a complex and dynamic environment, to perform interpretation and fusion of this data, and to present the resulting information to a human operator in a synthetic form that highlights features of interest of the environment. The system can then be regarded as a virtual instrument. A useful form to organize and present this information is a virtual spatial map-a representation of the environment in which colored geometric figures are placed to indicate that a given feature (or event) has been detected at that location. We illustrate our approach of building virtual instruments by presenting a case study of semi-autonomous remote environmental exploration. A mobile platform gathers information about a remote environment using multi-modal sensor data collection, information processing, and data fusion at different levels of abstraction and resolution. The result of the exploration is a fused virtual map that contains the important features of the environment

  • 1359.
    Wiedemann, Thomas
    Örebro University, School of Science and Technology.
    Domain Knowledge Assisted Robotic Exploration and Source Localization2020Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Deploying mobile robots to explore hazardous environments provides an advantageous way to avoid threats for human operators. For example, in situations, where airborne toxic or explosive material is leaking, robots can be dispatched to localize the leaks. This thesis investigates a novel exploration strategy to automatically localize such emission sources with multiple mobile robots that are equipped with sensors to measure the concentration of the emitted gas.

    The problem of localizing gas sources consists of two sub-problems that are addressed here. First, the thesis develops a method to estimate the source locations from sequences of localized concentration measurements. This approach can be also applied in case the measurements are collected by static sensor networks or human operators. Second, the thesis proposes an exploration strategy that guides mobile robots to informative measurement locations. With this strategy, a high level of autonomy is achieved and it is ensured that the collected measurements help to estimate the sources. As the main contribution, the proposed approach incorporates prior available domain knowledge about the gas dispersion process and the environment. Accordingly, the approach was coined Domain-knowledge Assisted Robotic Exploration and Source-localization (DARES). Domain knowledge is incorporated in two ways. First, the advection-diffusion Partial Differential Equation (PDE) provides a mathematical model of the gas dispersion process. A Bayesian interpretation of the PDE allows us to estimate the source distribution and to design the exploration strategy. Second, the additional assumption is exploited that the sources are sparsely distributed  in the environment, even though we do not know their exact number. The Bayesian inference approach incorporates this assumption by means of a sparsity inducing prior.

    Simulations and experiments show that the sparsity inducing prior helps to localize the sources based on fewer measurements compared to not exploiting the sparsity assumption. Further, the DARES approach results in efficient measurement patterns of the robots, which tend to start in downwind regions and move in upwind direction towards the sources where they cluster their measurements. It is remarkable that this behavior arises naturally without explicit instructions as a result of including domain knowledge and the proposed exploration strategy.

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    Domain Knowledge Assisted Robotic Exploration and Source Localization
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  • 1360.
    Wiedemann, Thomas
    et al.
    Institute of Communications and Navigation, German Aerospace Center (DLR) Oberpfaffenhofen, Germany.
    Schaab, Marius
    Institute of Communications and Navigation, German Aerospace Center (DLR) Oberpfaffenhofen, Germany.
    Gomez, Juan Marchal
    Institute of Communications and Navigation, German Aerospace Center (DLR) Oberpfaffenhofen, Germany.
    Shutin, Dmitriy
    Institute of Communications and Navigation, German Aerospace Center (DLR) Oberpfaffenhofen, Germany.
    Scheibe, Monika
    Institute of Atmospheric Physics, German Aerospace Center (DLR), Oberpfaffenhofen, Germany.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Gas Source Localization Based on Binary Sensing with a UAV2022In: 2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), IEEE , 2022Conference paper (Refereed)
    Abstract [en]

    Precise gas concentration measurements are often difficult, especially by in-situ sensors mounted on an Unmanned Aerial Vehicle (UAV). Simple gas detection, on the other hand, is more robust and reliable, yet brings significantly less information for gas source localization. In this paper, we compensate for the lack of information by a physical model of gas propagation based on the advection-diffusion Partial Differential Equation (PDE). By linking binary gas detection measurements to computed gas concentration using the physical model and an appropriately designed likelihood function, it becomes possible to identify the most likely gas source distribution. The approach was validated in two experiments with ethanol and smoke as "toy" gasses. It is shown that the method is able to successfully localize the source locations in experiments based on gas detection measurements taken by a UAV.

  • 1361.
    Wiedemann, Thomas
    et al.
    Institute of Communications and Navigation, German Aerospace Center (DLR), Wessling, Germany.
    Shutin, Dmitri
    Institute of Communications and Navigation, German Aerospace Center (DLR), Wessling, Germany.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Bayesian Gas Source Localization and Exploration with a Multi-Robot System Using Partial Differential Equation Based Modeling2017In: 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017): Proceedings, IEEE, 2017, p. 122-124Conference paper (Refereed)
    Abstract [en]

    Here we report on active water sampling devices forunderwater chemical sensing robots. Crayfish generate jetlikewater currents during food search by waving theflagella of their maxillipeds. The jets generated toward theirsides induce an inflow from the surroundings to the jets.Odor sample collection from the surroundings to theirolfactory organs is promoted by the generated inflow.Devices that model the jet discharge of crayfish have beendeveloped to investigate the effectiveness of the activechemical sampling. Experimental results are presented toconfirm that water samples are drawn to the chemicalsensors from the surroundings more rapidly by using theaxisymmetric flow field generated by the jet discharge thanby centrosymmetric flow field generated by simple watersuction. Results are also presented to show that there is atradeoff between the angular range of chemical samplecollection and the sample collection time.

  • 1362.
    Wilcox, Andreas
    Örebro University, School of Science and Technology.
    Simulation av Xbox Live Indie Games gränssnittet2011Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    This thesis was developed as an assignment from Ludosity Interactive where the goal was to develop a copy of the Xbox Live Indie Games-marketplace from the Xbox 360. Ludosity Interactive had a necessity to easily test a game's attractiveness to potential customers using testing people from outside the company in a simulated Xbox Live Indie Games test environment; excluding this developed system there is no other way to do such an analysis without actually releasing the game on the Xbox Live Indie Games marketplace and then analyze the resulting sales from the product.

    The finished system had to be similar to the original system to the degree that a user could see past the interface itself and use the system just as he/she would have used the real marketplace. It also had to be easy to change and add games to the system so that Ludosity Interactive easily could show the games and the data that they deemed interesting for their tests. The final product was developed using C#, XNA and XML together with an Agileinspired development method in combination with Pivotal Tracker. This report describes how this product was developed.

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  • 1363.
    Windefelt, Andreas
    Örebro University, School of Science and Technology.
    RekUpp – Utvecklingsprocessen bakom ett systemför självreflektion och kursutveckling2022Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    An obstacle in higher education has for a long time been that it is difficult forteachers to get an overview for how the students are performing. The parts of thecourse that are believed to be hard may not be, and vice versa. Beyond directquestions from students, teachers must guess from the best of their ability aboutwhich exercises that the students experience as difficult.Within certain technical and mathematical educations, it is common for teachersto recommend exercises that the students are expected to complete to get anunderstanding of the field. There is a risk that students don’t do these exercisescontinuously and instead spend a lot of time studying ahead of an exam at the endof the course. This does not give the same deep knowledge as continuous studydoes.Teachers tend to publish these exercises in a text file on an available learningplatform, this does not solve the problems mentioned previously. A suggestion fora solution is RekUpp (from the Swedish translation of Recommended exercises), aweb application that teachers can use to publish which exercises students aremeant to do. The program is tailored to engage students in self-reflection abouttheir performance and give them prerequisites for better time management. Theinformation and data that is created can be used by teachers to analyze how thestudents are performing. The system is expected to contribute to solving both theproblem of a lack of information and to continuously engage students.In this report the development process is described. This is based on previousresearch about self-regulation, self-reflection, and digital tools to engage studentsin the best possible way and at the same time transfer information that teacherscan use to adjust and improve their courses. The intention is that the developmentof the program, and the conclusions that are presented, can be used as a startingpointor framework to develop similar systems.

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  • 1364.
    Winkler, Nicolas P.
    et al.
    Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Kotlyar, Oleksandr
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Fan, Han
    Örebro University, School of Science and Technology.
    Matsukura, Haruka
    University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, Japan.
    Ishida, Hiroshi
    Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei, Tokyo, Japan.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Learning From the Past: Sequential Deep Learning for Gas Distribution Mapping2022In: ROBOT2022: Fifth Iberian Robotics Conference: Advances in Robotics, Volume 2 / [ed] Danilo Tardioli; Vicente Matellán; Guillermo Heredia; Manuel F. Silva; Lino Marques, Springer, 2022, Vol. 590, p. 178-188Conference paper (Refereed)
    Abstract [en]

    To better understand the dynamics in hazardous environments, gas distribution mapping aims to map the gas concentration levels of a specified area precisely. Sampling is typically carried out in a spatially sparse manner, either with a mobile robot or a sensor network and concentration values between known data points have to be interpolated. In this paper, we investigate sequential deep learning models that are able to map the gas distribution based on a multiple time step input from a sensor network. We propose a novel hybrid convolutional LSTM - transpose convolutional structure that we train with synthetic gas distribution data. Our results show that learning the spatial and temporal correlation of gas plume patterns outperforms a non-sequential neural network model.

  • 1365.
    Winkler, Nicolas P.
    et al.
    Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Matsukura, Haruka
    University of Electro-Communications, Tokyo, Japan.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Ishida, Hiroshi
    Tokyo University of Agriculture and Technology, Tokyo, Japan.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Super-Resolution for Gas Distribution Mapping: Convolutional Encoder-Decoder Network2022In: 2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), IEEE , 2022Conference paper (Refereed)
    Abstract [en]

    Gas distribution mapping is important to have an accurate understanding of gas concentration levels in hazardous environments. A major problem is that in-situ gas sensors are only able to measure concentrations at their specific location. The gas distribution in-between the sampling locations must therefore be modeled. In this research, we interpret the task of spatial interpolation between sparsely distributed sensors as a task of enhancing an image's resolution, namely super-resolution. Because autoencoders are proven to perform well for this super-resolution task, we trained a convolutional encoder-decoder neural network to map the gas distribution over a spatially sparse sensor network. Due to the difficulty to collect real-world gas distribution data and missing ground truth, we used synthetic data generated with a gas distribution simulator for training and evaluation of the model. Our results show that the neural network was able to learn the behavior of gas plumes and outperforms simpler interpolation techniques.

  • 1366.
    Winkler, Nicolas P.
    et al.
    Örebro University, School of Science and Technology. Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Using Redundancy in a Sensor Network to Compensate Sensor Failures2021In: 2021 IEEE SENSORS, IEEE , 2021Conference paper (Refereed)
    Abstract [en]

    Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks.

  • 1367.
    Winkler, Nicolas P.
    et al.
    Örebro University, School of Science and Technology. Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Säämänen, Arto
    Occupational Safety, Finnish Institute of Occupational Health, Tampere, Finland.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    High-quality meets low-cost: Approaches for hybrid-mobility sensor networks2020In: MATERIALS TODAY-PROCEEDINGS, Elsevier, 2020, Vol. 32, p. 250-253Conference paper (Refereed)
    Abstract [en]

    Air pollution within industrial scenarios is a major risk for workers, which is why detailed knowledge about the dispersion of dusts and gases is necessary. This paper introduces a system combining stationary low-cost and high-quality sensors, carried by ground robots and unmanned aerial vehicles. Based on these dense sampling capabilities, detailed distribution maps of dusts and gases will be created. This system enables various research opportunities, especially on the fields of distribution mapping and sensor planning. Standard approaches for distribution mapping can be enhanced with knowledge about the environment's characteristics, while the effectiveness of new approaches, utilizing neural networks, can be further investigated. The influence of different sensor network setups on the predictive quality of distribution algorithms will be researched and metrics for the quantification of a sensor network's quality will be investigated.

  • 1368.
    Winters, Thomas
    et al.
    Department of Computer Science, Leuven.AI, KU Leuven, Belgium.
    Marra, Giuseppe
    Department of Computer Science, Leuven.AI, KU Leuven, Belgium.
    Manhaeve, Robin
    Department of Computer Science, Leuven.AI, KU Leuven, Belgium.
    De Raedt, Luc
    Örebro University, School of Science and Technology. Department of Computer Science, Leuven.AI, KU Leuven, Belgium.
    DeepStochLog: Neural Stochastic Logic Programming (Extended Abstract)2022In: Proceedings 38th International Conference on Logic Programming, Haifa, Israel, 31st July 2022 - 6th August 2022 / [ed] Yuliya Lierler; Jose F. Morales; Carmine Dodaro; Veronica Dahl; Martin Gebser; Tuncay Tekle, Open Publishing Association , 2022, Vol. 364, p. 126-128Conference paper (Refereed)
  • 1369.
    Wärme, Albin
    Örebro University, School of Science and Technology.
    Framework for evaluating intrinsic kidnapping detection methods in Monte Carlo Localisation2021Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    This thesis presents a framework for evaluating intrinsic kidnapped robot problemdetection methods. The framework implements a landmark based environmentin which a robot with a 360range  nder sensor acts and navigates itself tobe subjected to the kidnapped robot problem through di erent causes such asteleportation and drifting odometry. A set of arguments are presented arguingagainst performing the task in ROS using the ROS-amcl package. Validationexperiments are performed on the framework and through surveying the state ofthe art solutions for the kidnapped robot problem, two easy to use kidnappingdetection methods are implemented and tested. The kidnapped robot problemin Monte Carlo Localisation(MCL) is discussed and the practical importance ofthe problem as well as possible causes are explained and discussed.

  • 1370.
    Xing, Yuxin
    et al.
    School of Engineering, University of Warwick, Coventry, UK.
    Vincent, Timothy A.
    School of Engineering, University of Warwick, Coventry, UK.
    Cole, Marina
    School of Engineering, University of Warwick, Coventry, UK.
    Gardner, Julian W.
    School of Engineering, University of Warwick, Coventry, UK.
    Fan, Han
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Mobile robot multi-sensor unit for unsupervised gas discrimination in uncontrolled environments2017In: IEEE SENSORS 2017: Conference Proceedings, New York: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1691-1693Conference paper (Refereed)
    Abstract [en]

    In this work we present a novel multi-sensor unit to detect and discriminate unknown gases in uncontrolled environments. The unit includes three metal oxide (MOX) sensors with CMOS micro heaters, a plasmonic enhanced non-dispersive infra-red (NDIR) sensor, a commercial temperature humidity sensor, and a flow sensor. The proposed sensing unit was evaluated with plumes of gases (propanol, ethanol and acetone) in both, a laboratory setup on a gas testing bench and on-board a mobile robot operating in an indoor workshop. It offers significantly improved performance compared to commercial systems, in terms of power consumption, response time and physical size. We verified the ability to discriminate gases in an unsupervised manner, with data collected on the robot and high accuracy was obtained in the classification of propanol versus acetone (96%), and ethanol versus acetone (90%).

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    Mobile Robot Multi-sensor Unit for Unsupervised Gas Discrimination in Uncontrolled Environments
  • 1371.
    Yang, Quantao
    Örebro University, School of Science and Technology.
    Robot Skill Acquisition through Prior-Conditioned Reinforcement Learning2023Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Advancements in robotics and artificial intelligence have paved the way for autonomous agents to perform complex tasks in various domains. A critical challenge in the field of robotics is enabling robots to acquire and refine skills efficiently, allowing them to adapt and excel in diverse environments. This thesis investigates the questions of how to acquire robot skills through priorconstrained machine learning and adapt these learned skills to novel environments safely and efficiently.

    The thesis leverages the synergy between Reinforcement Learning (RL) and prior knowledge to facilitate skill acquisition in robots. It integrates existing task constraints, domain knowledge and contextual information into the learning process, enabling the robot to acquire new skills efficiently. The core idea behind our method is to exploit structured priors derived from both expert demonstrations and domain-specific information which guide the RL process to effectively explore and exploit the state-action space.

    The first contribution lies in guaranteeing the execution of safe actions and preventing constraint violations during the exploration phase of RL. By incorporating task-specific constraints, the robot avoids entering into regions of the environment where potential risks or failures may occur. It allows for efficient exploration of the action space while maintaining safety, making it well-suited for scenarios where continuous actions need to adhere to specific constraints. The second contribution addresses the challenge of learning a policy on a real robot to accomplish contact-rich tasks by exploiting a set of pre-collected demonstrations. Specifically, a variable impedance action space is leveraged to enable the system to effectively adapt its interactions during contact-rich manipulation tasks. In the third contribution, the thesis explores the transferability of skills acquired across different tasks and domains, highlighting the framework’s potential for building a repository of reusable skills. By comparing the similarity between the target task and the prior tasks, prior knowledge is combined to guide the policy learning process for new tasks. In the fourth contribution of this thesis, we introduce a cycle generative model to transfer acquired skills across different robot platforms by learning from unstructured prior demonstrations. In summary, the thesis introduces a novel paradigm for advancing the field of robotic skill acquisition by synergizing prior knowledge with RL.

    List of papers
    1. Null space based efficient reinforcement learning with hierarchical safety constraints
    Open this publication in new window or tab >>Null space based efficient reinforcement learning with hierarchical safety constraints
    2021 (English)In: 2021 European Conference on Mobile Robots (ECMR), IEEE, 2021Conference paper, Published paper (Refereed)
    Abstract [en]

    Reinforcement learning is inherently unsafe for use in physical systems, as learning by trial-and-error can cause harm to the environment or the robot itself. One way to avoid unpredictable exploration is to add constraints in the action space to restrict the robot behavior. In this paper, we proposea null space based framework of integrating reinforcement learning methods in constrained continuous action spaces. We leverage a hierarchical control framework to decompose target robotic skills into higher ranked tasks (e. g., joint limits and obstacle avoidance) and lower ranked reinforcement learning task. Safe exploration is guaranteed by only learning policies in the null space of higher prioritized constraints. Meanwhile multiple constraint phases for different operational spaces are constructed to guide the robot exploration. Also, we add penalty loss for violating higher ranked constraints to accelerate the learning procedure. We have evaluated our method on different redundant robotic tasks in simulation and show that our null space based reinforcement learning method can explore and learn safely and efficiently.

    Place, publisher, year, edition, pages
    IEEE, 2021
    National Category
    Robotics
    Identifiers
    urn:nbn:se:oru:diva-95146 (URN)10.1109/ECMR50962.2021.9568848 (DOI)000810510000061 ()9781665412131 (ISBN)
    Conference
    European Conference on Mobile Robots (ECMR 2021), Virtual meeting, August 31 - September 3, 2021
    Note

    Funding agency:

    Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP)

    Available from: 2021-10-21 Created: 2021-10-21 Last updated: 2023-10-06Bibliographically approved
    2. Variable Impedance Skill Learning for Contact-Rich Manipulation
    Open this publication in new window or tab >>Variable Impedance Skill Learning for Contact-Rich Manipulation
    Show others...
    2022 (English)In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 7, no 3, p. 8391-8398Article in journal, Letter (Refereed) Published
    Abstract [en]

    Contact-rich manipulation tasks remain a hard problem in robotics that requires interaction with unstructured environments. Reinforcement Learning (RL) is one potential solution to such problems, as it has been successfully demonstrated on complex continuous control tasks. Nevertheless, current state-of-the-art methods require policy training in simulation to prevent undesired behavior and later domain transfer even for simple skills involving contact. In this paper, we address the problem of learning contact-rich manipulation policies by extending an existing skill-based RL framework with a variable impedance action space. Our method leverages a small set of suboptimal demonstration trajectories and learns from both position, but also crucially impedance-space information. We evaluate our method on a number of peg-in-hole task variants with a Franka Panda arm and demonstrate that learning variable impedance actions for RL in Cartesian space can be deployed directly on the real robot, without resorting to learning in simulation.

    Place, publisher, year, edition, pages
    IEEE Press, 2022
    Keywords
    Machine learning for robot control, reinforcement learning, variable impedance control
    National Category
    Robotics
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-100386 (URN)10.1109/LRA.2022.3187276 (DOI)000838455200009 ()2-s2.0-85133737407 (Scopus ID)
    Funder
    Knut and Alice Wallenberg Foundation
    Available from: 2022-08-01 Created: 2022-08-01 Last updated: 2024-01-17
    3. MPR-RL: Multi-Prior Regularized Reinforcement Learning for Knowledge Transfer
    Open this publication in new window or tab >>MPR-RL: Multi-Prior Regularized Reinforcement Learning for Knowledge Transfer
    2022 (English)In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 7, no 3, p. 7652-7659Article in journal (Refereed) Published
    Abstract [en]

    In manufacturing, assembly tasks have been a challenge for learning algorithms due to variant dynamics of different environments. Reinforcement learning (RL) is a promising framework to automatically learn these tasks, yet it is still not easy to apply a learned policy or skill, that is the ability of solving a task, to a similar environment even if the deployment conditions are only slightly different. In this letter, we address the challenge of transferring knowledge within a family of similar tasks by leveraging multiple skill priors. We propose to learn prior distribution over the specific skill required to accomplish each task and compose the family of skill priors to guide learning the policy for a new task by comparing the similarity between the target task and the prior ones. Our method learns a latent action space representing the skill embedding from demonstrated trajectories for each prior task. We have evaluated our method on a task in simulation and a set of peg-in-hole insertion tasks and demonstrate better generalization to new tasks that have never been encountered during training. Our Multi-Prior Regularized RL (MPR-RL) method is deployed directly on a real world Franka Panda arm, requiring only a set of demonstrated trajectories from similar, but crucially not identical, problem instances.

    Place, publisher, year, edition, pages
    IEEE Press, 2022
    Keywords
    Machine Learning for Robot Control, Reinforcement Learning, Transfer Learning
    National Category
    Robotics
    Identifiers
    urn:nbn:se:oru:diva-99762 (URN)10.1109/LRA.2022.3184805 (DOI)000818872000024 ()2-s2.0-85133574877 (Scopus ID)
    Note

    Funding agency:

    Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

    Available from: 2022-06-28 Created: 2022-06-28 Last updated: 2024-01-17Bibliographically approved
    4. Learn from Robot: Transferring Skills for Diverse Manipulation via Cycle Generative Networks
    Open this publication in new window or tab >>Learn from Robot: Transferring Skills for Diverse Manipulation via Cycle Generative Networks
    2023 (English)In: 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), IEEE conference proceedings, 2023Conference paper, Published paper (Refereed)
    Abstract [en]

    Reinforcement learning (RL) has shown impressive results on a variety of robot tasks, but it requires a large amount of data for learning a single RL policy. However, in manufacturing there is a wide demand of reusing skills from different robots and it is hard to transfer the learned policy to different hardware due to diverse robot body morphology, kinematics, and dynamics. In this paper, we address the problem of transferring policies between different robot platforms. We learn a set of skills on each specific robot and represent them in a latent space. We propose to transfer the skills between different robots by mapping latent action spaces through a cycle generative network in a supervised learning manner. We extend the policy model learned on one robot with a pre-trained generative network to enable the robot to learn from the skill of another robot. We evaluate our method on several simulated experiments and demonstrate that our Learn from Robot (LfR) method accelerates new skill learning.

    Place, publisher, year, edition, pages
    IEEE conference proceedings, 2023
    Series
    IEEE International Conference on Automation Science and Engineering, ISSN 2161-8070, E-ISSN 2161-8089
    Keywords
    Reinforcement Learning, Transfer Learning, Generative Models
    National Category
    Robotics
    Identifiers
    urn:nbn:se:oru:diva-108719 (URN)10.1109/CASE56687.2023.10260484 (DOI)9798350320701 (ISBN)9798350320695 (ISBN)
    Conference
    19th International Conference on Automation Science and Engineering (IEEE CASE 2023), Cordis, Auckland, New Zealand, August 26-30, 2023
    Funder
    Wallenberg AI, Autonomous Systems and Software Program (WASP)
    Available from: 2023-10-03 Created: 2023-10-03 Last updated: 2024-03-07Bibliographically approved
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    Robot Skill Acquisition through Prior-Conditioned Reinforcement Learning
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  • 1372.
    Yang, Quantao
    et al.
    Örebro University, School of Science and Technology.
    Dürr, Alexander
    Department of Computer Science, Lund University, Sweden.
    Topp, Elin Anna
    Department of Computer Science, Lund University, Sweden.
    Stork, Johannes Andreas
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Learning Impedance Actions for Safe Reinforcement Learning in Contact-Rich Tasks2021In: NeurIPS 2021 Workshop on Deployable Decision Making in Embodied Systems (DDM), 2021Conference paper (Other academic)
    Abstract [en]

    Reinforcement Learning (RL) has the potential of solving complex continuous control tasks, with direct applications to robotics. Nevertheless, current state-of-the-art methods are generally unsafe to learn directly on a physical robot as exploration by trial-and-error can cause harm to the real world systems. In this paper, we leverage a framework for learning latent action spaces for RL agents from demonstrated trajectories. We extend this framework by connecting it to a variable impedance Cartesian space controller, allowing us to learn contact-rich tasks safely and efficiently. Our method learns from trajectories that incorporate both positional, but also crucially impedance-space information. We evaluate our method on a number of peg-in-hole task variants with a Franka Panda arm and demonstrate that learning variable impedance actions for RL in Cartesian space can be safely deployed on the real robot directly, without resorting to learning in simulation and a subsequent policy transfer.

    Download full text (pdf)
    Learning Impedance Actions for Safe Reinforcement Learning in Contact-Rich Tasks
  • 1373.
    Yang, Wen-chi
    et al.
    Department of Computer Science, Leuven.AI, KU Leuven, Leuven, Belgium.
    Jain, Arcchit
    Department of Computer Science, Leuven.AI, KU Leuven, Leuven, Belgium.
    De Raedt, Luc
    Örebro University, School of Science and Technology. Department of Computer Science, Leuven.AI, KU Leuven, Leuven, Belgium.
    Meert, Wannes
    Department of Computer Science, Leuven.AI, KU Leuven, Leuven, Belgium.
    Parameter Learning in ProbLog With Annotated Disjunctions2022In: Advances in Intelligent Data Analysis XX: 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20–22, 2022, Proceedings, Springer, 2022, p. 378-391Conference paper (Refereed)
    Abstract [en]

    In parameter learning, a partial interpretation most often contains information about only a subset of the parameters in the program. However, standard EM-based algorithms use all interpretations to learn all parameters, which significantly slows down learning. To tackle this issue, we introduce EMPLiFI, an EM-based parameter learning technique for probabilistic logic programs, that improves the efficiency of EM by exploiting the rule-based structure of logic programs. In addition, EMPLiFI enables parameter learning of multi-head annotated disjunctions in ProbLog programs, which was not yet possible in previous methods. Theoretically, we show that EMPLiFI is correct. Empirically, we compare EMPLiFI to LFI-ProbLog and EMBLEM. The results show that EMPLiFI is the most efficient in learning single-head annotated disjunctions. In learning multi-head annotated disjunctions, EMPLiFI is more accurate than EMBLEM, while LFI-ProbLog cannot handle this task.

  • 1374.
    Yang, Wen-Chi
    et al.
    Leuven AI, KU Leuven, Belgium.
    Marra, Giuseppe
    Leuven AI, KU Leuven, Belgium.
    Rens, Gavin
    Stellenbosch University, South Africa.
    De Raedt, Luc
    Örebro University, School of Science and Technology. Leuven AI, KU Leuven, Belgium.
    Safe Reinforcement Learning via Probabilistic Logic Shields2023In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI 2023) / [ed] Edith Elkind, International Joint Conferences on Artificial Intelligence , 2023, p. 5739-5749Conference paper (Refereed)
    Abstract [en]

    Safe Reinforcement learning (Safe RL) aims at learning optimal policies while staying safe. A popular solution to Safe RL is shielding, which uses a logical safety specification to prevent an RL agent from taking unsafe actions. However, traditional shielding techniques are difficult to integrate with continuous, end-to-end deep RL methods. To this end, we introduce Probabilistic Logic Policy Gradient (PLPG). PLPG is a model-based Safe RL technique that uses probabilistic logic programming to model logical safety constraints as differentiable functions. Therefore, PLPG can be seamlessly applied to any policy gradient algorithm while still providing the same convergence guarantees. In our experiments, we show that PLPG learns safer and more rewarding policies compared to other state-of-the-art shielding techniques. 

  • 1375.
    Yang, Wen-Chi
    et al.
    Department of Computer Science, KU Leuven, Leuven, Belgium.
    Raskin, Jean-Francois
    Université libre de Bruxelles, Campus de la Plaine, Bruxelles, Belgium.
    De Raedt, Luc
    Örebro University, School of Science and Technology.
    Lifted model checking for relational MDPs2022In: Machine Learning, ISSN 0885-6125, E-ISSN 1573-0565, no 111, p. 3797-3838Article in journal (Refereed)
    Abstract [en]

    Probabilistic model checking has been developed for verifying systems that have stochastic and nondeterministic behavior. Given a probabilistic system, a probabilistic model checker takes a property and checks whether or not the property holds in that system. For this reason, probabilistic model checking provide rigorous guarantees. So far, however, probabilistic model checking has focused on propositional models where a state is represented by a symbol. On the other hand, it is commonly required to make relational abstractions in planning and reinforcement learning. Various frameworks handle relational domains, for instance, STRIPS planning and relational Markov Decision Processes. Using propositional model checking in relational settings requires one to ground the model, which leads to the well known state explosion problem and intractability. We present pCTL-REBEL, a lifted model checking approach for verifying pCTL properties of relational MDPs. It extends REBEL, a relational model-based reinforcement learning technique, toward relational pCTL model checking. PCTL-REBEL is lifted, which means that rather than grounding, the model exploits symmetries to reason about a group of objects as a whole at the relational level. Theoretically, we show that pCTL model checking is decidable for relational MDPs that have a possibly infinite domain, provided that the states have a bounded size. Practically, we contribute algorithms and an implementation of lifted relational model checking, and we show that the lifted approach improves the scalability of the model checking approach.

  • 1376.
    Yang, Yuxuan
    Örebro University, School of Science and Technology.
    Advancing Modeling and Tracking of Deformable Linear Objects for Real-World Applications2023Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Deformable linear objects (DLOs), such as cables, wires, ropes, and sutures, are important components in various applications in robotics. Although automating DLO manipulation tasks through robot deployment can offer benefits in terms of cost reduction and increased efficiency, it presents difficult challenges. Unlike rigid objects, DLOs can deform and possess high-dimensionalstate space, significantly amplifying the complexity of their dynamics. These inherent characteristics, combined with the absence of distinctive features and the occurrence of occlusion, contribute to the difficulties involved in DLO manipulation tasks.

    This dissertation focuses on developing novel approaches for two aspects: modeling and tracking DLOs. Both aspects are important in DLO manipulation, yet they remain open research questions. Current analytical physics-based methods for modeling DLO dynamics are either time-consuming or inaccurate and often undifferentiable, which hampers their applications in robot planning and control. Although deep learning methods have shown promise in modeling object dynamics, there is still a gap in learning DLO dynamics in a 3D environment. As for the tracking, many current methods rely on assumptions such as knowing the DLO initial state and segmented DLO point sets, which are rarely fulfilled in real-world scenarios, significantly limiting their practical applicability.

    This dissertation aims to answer three research questions: How can data-driven models be used for learning DLO dynamics? How can the data-driven models be efficiently trained for real-world DLO manipulation tasks? How can images be used to track the state of DLOs during manipulation in uncontrolled real-world settings?

    The first contribution of this dissertation is a data-driven model that effectively simulates DLO state transitions. To bridge the current gap in learning full 3D DLO dynamics, a new DLO representation and a recurrent network module are introduced to facilitate better effect propagation between different segments along the DLO. Meanwhile, the model is differentiable, enabling efficient model predictive control for real-world DLO shape control tasks. However, data-driven approaches demand a large amount of training data, which can be time-consuming and laborious to collect in practice. Thus, the second and third contributions propose two frameworks for minimizing the burden incurred by the data collection process. Specifically, a framework is proposed for learning the data-driven model on synthetic data from simulation. Parameters of the simulation model are identified by solving an optimization problem using the differential evolution algorithm with only a few trajectories of a real DLO required. This dissertation also proposes a trial-and-error interaction approach inspired by model-based reinforcement learning, which significantly reduces the need for training data and automates the data collection process.

    The above contributions rely on artificial markers for tracking the DLO state during data collection and closed-loop control, which is acknowledged as a limitation. To address this, the fourth contribution proposes a novel approach that utilizes a particle filter within a low-dimensional state embedding learned by an autoencoder. This approach achieves robust tracking under occlusion and eliminates the need for high-fidelity physics simulations or manually designed constraints. Furthermore, the particle-filter-based method is employed and extended to track the state of a branched deformable linear object (BDLO), which is more challenging because of its complex branched structure. The proposed approach learns a likelihood prediction function directly from depth images in simulation, without requiring segmented point sets of the BDLO.

    In conclusion, with the proposed methods for modeling and tracking DLOs, this dissertation contributes to advancing a broad range of applications, including DLO simulation, tracking, and manipulation. The development of these approaches lays the foundations for various directions of future research, which are further discussed in the dissertation.

    List of papers
    1. Learning to Propagate Interaction Effects for Modeling Deformable Linear Objects Dynamics
    Open this publication in new window or tab >>Learning to Propagate Interaction Effects for Modeling Deformable Linear Objects Dynamics
    2021 (English)In: 2021 IEEE International Conference on Robotics and Automation (ICRA): IEEE International Conference on Robotics and Automation (ICRA 2021), Xi'an, China, May 30 - June 5, 2021, IEEE, 2021, p. 1950-1957Conference paper, Published paper (Refereed)
    Abstract [en]

    Modeling dynamics of deformable linear objects (DLOs), such as cables, hoses, sutures, and catheters, is an important and challenging problem for many robotic manipulation applications. In this paper, we propose the first method to model and learn full 3D dynamics of DLOs from data. Our approach is capable of capturing the complex twisting and bending dynamics of DLOs and allows local effects to propagate globally. To this end, we adapt the interaction network (IN) dynamics learning method for capturing the interaction between neighboring segments in a DLO and augment it with a recurrent model for propagating interaction effects along the length of a DLO. For learning twisting and bending dynamics in 3D, we also introduce a new suitable representation of DLO segments and their relationships. Unlike the original IN method, our model learns to propagate the effects of local interaction between neighboring segments to each segment in the chain within a single time step, without the need for iterated propagation steps. Evaluation of our model with synthetic and newly collected real-world data shows better accuracy and generalization in short-term and long-term predictions than the current state of the art. We further integrate our learned model in a model predictive control scheme and use it to successfully control the shape of a DLO. Our implementation is available at https : //gitsvn-nt.oru.se/ammlab-public/in-bilstm.

    Place, publisher, year, edition, pages
    IEEE, 2021
    Series
    2021 IEEE International Conference on Robotics and Automation (ICRA), ISSN 1050-4729, E-ISSN 2577-087X
    National Category
    Robotics
    Identifiers
    urn:nbn:se:oru:diva-95166 (URN)10.1109/ICRA48506.2021.9561636 (DOI)000765738801113 ()2-s2.0-85116832046 (Scopus ID)9781728190778 (ISBN)9781728190785 (ISBN)
    Conference
    IEEE International Conference on Robotics and Automation (ICRA 2021), Xi'an, China, May 30 - June 5, 2021
    Funder
    Vinnova, 2019-05175Vinnova, 2017-02205Wallenberg AI, Autonomous Systems and Software Program (WASP)
    Available from: 2021-11-10 Created: 2021-11-10 Last updated: 2023-09-18Bibliographically approved
    2. Learning differentiable dynamics models for shape control of deformable linear objects
    Open this publication in new window or tab >>Learning differentiable dynamics models for shape control of deformable linear objects
    2022 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 158, article id 104258Article in journal (Refereed) Published
    Abstract [en]

    Robots manipulating deformable linear objects (DLOs) – such as surgical sutures in medical robotics, or cables and hoses in industrial assembly – can benefit substantially from accurate and fast differentiable predictive models. However, the off-the-shelf analytic physics models fall short of differentiability. Recently, neural-network-based data-driven models have shown promising results in learning DLO dynamics. These models have additional advantages compared to analytic physics models, as they are differentiable and can be used in gradient-based trajectory planning. Still, the data-driven approaches demand a large amount of training data, which can be challenging for real-world applications. In this paper, we propose a framework for learning a differentiable data-driven model for DLO dynamics with a minimal set of real-world data. To learn DLO twisting and bending dynamics in a 3D environment, we first introduce a new suitable DLO representation. Next, we use a recurrent network module to propagate effects between different segments along a DLO, thereby addressing a critical limitation of current state-of-the-art methods. Then, we train a data-driven model on synthetic data generated in simulation, instead of foregoing the time-consuming and laborious data collection process for real-world applications. To achieve a good correspondence between real and simulated models, we choose a set of simulation model parameters through parameter identification with only a few trajectories of a real DLO required. We evaluate several optimization methods for parameter identification and demonstrate that the differential evolution algorithm is efficient and effective for parameter identification. In DLO shape control tasks with a model-based controller, the data-driven model trained on synthetic data generated by the resulting models performs on par with the ones trained with a comparable amount of real-world data which, however, would be intractable to collect.

    Place, publisher, year, edition, pages
    Elsevier, 2022
    Keywords
    Deformable linear object, Model learning, Parameter identification, Model predictive control
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:oru:diva-101292 (URN)10.1016/j.robot.2022.104258 (DOI)000869528600006 ()2-s2.0-85138188346 (Scopus ID)
    Funder
    Vinnova, 2019-05175Knut and Alice Wallenberg Foundation
    Available from: 2022-09-19 Created: 2022-09-19 Last updated: 2023-09-18Bibliographically approved
    3. Online Model Learning for Shape Control of Deformable Linear Objects
    Open this publication in new window or tab >>Online Model Learning for Shape Control of Deformable Linear Objects
    2022 (English)In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2022, p. 4056-4062Conference paper, Published paper (Refereed)
    Abstract [en]

    Traditional approaches to manipulating the state of deformable linear objects (DLOs) - i.e., cables, ropes - rely on model-based planning. However, constructing an accurate dynamic model of a DLO is challenging due to the complexity of interactions and a high number of degrees of freedom. This renders the task of achieving a desired DLO shape particularly difficult and motivates the use of model-free alternatives, which while maintaining generality suffer from a high sample complexity. In this paper, we bridge the gap between these fundamentally different approaches and propose a framework that learns dynamic models of DLOs through trial-and-error interaction. Akin to model-based reinforcement learning (RL), we interleave learning and exploration to solve a 3D shape control task for a DLO. Our approach requires only a fraction of the interaction samples of the current state-of-the-art model-free RL alternatives to achieve superior shape control performance. Unlike offline model learning, our approach does not require expert knowledge for data collection, retains the ability to explore, and automatically selects relevant experience.

    Place, publisher, year, edition, pages
    IEEE, 2022
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:oru:diva-103194 (URN)10.1109/IROS47612.2022.9981080 (DOI)000908368203013 ()9781665479271 (ISBN)9781665479288 (ISBN)
    Conference
    35th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japan, October 23-27, 2022
    Funder
    Wallenberg AI, Autonomous Systems and Software Program (WASP)Vinnova, SIP-STRIM projects 2019-05175
    Available from: 2023-01-16 Created: 2023-01-16 Last updated: 2023-09-18
    4. Particle Filters in Latent Space for Robust Deformable Linear Object Tracking
    Open this publication in new window or tab >>Particle Filters in Latent Space for Robust Deformable Linear Object Tracking
    2022 (English)In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 7, no 4, p. 12577-12584Article in journal (Refereed) Published
    Abstract [en]

    Tracking of deformable linear objects (DLOs) is important for many robotic applications. However, achieving robust and accurate tracking is challenging due to the lack of distinctive features or appearance on the DLO, the object's high-dimensional state space, and the presence of occlusion. In this letter, we propose a method for tracking the state of a DLO by applying a particle filter approach within a lower-dimensional state embedding learned by an autoencoder. The dimensionality reduction preserves state variation, while simultaneously enabling a particle filter to accurately track DLO state evolution with a practically feasible number of particles. Compared to previous works, our method requires neither running a high-fidelity physics simulation, nor manual designs of constraints and regularization. Without the assumption of knowing the initial DLO state, our method can achieve accurate tracking even under complex DLO motions and in the presence of severe occlusions.

    Place, publisher, year, edition, pages
    IEEE, 2022
    Keywords
    Deep learning for visual perception, perception for grasping and manipulation, RGB-D perception
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:oru:diva-102576 (URN)10.1109/LRA.2022.3216985 (DOI)000886312200014 ()
    Funder
    Vinnova, 2020-04467Knut and Alice Wallenberg Foundation
    Available from: 2022-12-07 Created: 2022-12-07 Last updated: 2024-01-17Bibliographically approved
    5. Learn to Predict Posterior Probability in Particle Filtering for Tracking Deformable Linear Objects
    Open this publication in new window or tab >>Learn to Predict Posterior Probability in Particle Filtering for Tracking Deformable Linear Objects
    2022 (English)In: 3rd Workshop on Robotic Manipulation of Deformable Objects: Challenges in Perception, Planning and Control for Soft Interaction (ROMADO-SI), IROS 2022, Kyoto, Japan, 2022Conference paper, Published paper (Refereed)
    Abstract [en]

    Tracking deformable linear objects (DLOs) is a key element for applications where robots manipulate DLOs. However, the lack of distinctive features or appearance on the DLO and the object’s high-dimensional state space make tracking challenging and still an open question in robotics. In this paper, we propose a method for tracking the state of a DLO by applying a particle filter approach, where the posterior probability of each sample is estimated by a learned predictor. Our method can achieve accurate tracking even with no prerequisite segmentation which many related works require. Due to the differentiability of the posterior probability predictor, our method can leverage the gradients of posterior probabilities with respect to the latent states to improve the motion model in the particle filter. The preliminary experiments suggest that the proposed method can provide robust tracking results and the estimated DLO state converges quickly to the true state if the initial state is unknown.

    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:oru:diva-102743 (URN)
    Conference
    35th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japan, October 24-26, 2022
    Funder
    Vinnova, 2019-05175Wallenberg AI, Autonomous Systems and Software Program (WASP)
    Available from: 2023-01-27 Created: 2023-01-27 Last updated: 2023-09-18Bibliographically approved
    6. Tracking Branched Deformable Linear Objects Using Particle Filtering on Depth Images
    Open this publication in new window or tab >>Tracking Branched Deformable Linear Objects Using Particle Filtering on Depth Images
    (English)Manuscript (preprint) (Other academic)
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:oru:diva-108324 (URN)
    Available from: 2023-09-18 Created: 2023-09-18 Last updated: 2023-09-18Bibliographically approved
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    Download (pdf)
    Cover
    Download (pdf)
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  • 1377.
    Yang, Yuxuan
    et al.
    Örebro University, School of Science and Technology.
    Stork, Johannes A.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Tracking Branched Deformable Linear Objects Using Particle Filtering on Depth ImagesManuscript (preprint) (Other academic)
  • 1378.
    Yang, Yuxuan
    et al.
    Örebro University, School of Science and Technology.
    Stork, Johannes Andreas
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Learning differentiable dynamics models for shape control of deformable linear objects2022In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 158, article id 104258Article in journal (Refereed)
    Abstract [en]

    Robots manipulating deformable linear objects (DLOs) – such as surgical sutures in medical robotics, or cables and hoses in industrial assembly – can benefit substantially from accurate and fast differentiable predictive models. However, the off-the-shelf analytic physics models fall short of differentiability. Recently, neural-network-based data-driven models have shown promising results in learning DLO dynamics. These models have additional advantages compared to analytic physics models, as they are differentiable and can be used in gradient-based trajectory planning. Still, the data-driven approaches demand a large amount of training data, which can be challenging for real-world applications. In this paper, we propose a framework for learning a differentiable data-driven model for DLO dynamics with a minimal set of real-world data. To learn DLO twisting and bending dynamics in a 3D environment, we first introduce a new suitable DLO representation. Next, we use a recurrent network module to propagate effects between different segments along a DLO, thereby addressing a critical limitation of current state-of-the-art methods. Then, we train a data-driven model on synthetic data generated in simulation, instead of foregoing the time-consuming and laborious data collection process for real-world applications. To achieve a good correspondence between real and simulated models, we choose a set of simulation model parameters through parameter identification with only a few trajectories of a real DLO required. We evaluate several optimization methods for parameter identification and demonstrate that the differential evolution algorithm is efficient and effective for parameter identification. In DLO shape control tasks with a model-based controller, the data-driven model trained on synthetic data generated by the resulting models performs on par with the ones trained with a comparable amount of real-world data which, however, would be intractable to collect.

  • 1379.
    Yara, Ahmad
    Örebro University, School of Science and Technology.
    Preventing Vulnerabilities and MitigatingAttacks on the MQTT Protocol2020Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [sv]

    Syftet med denna studie är att undersöka och förstå hur säkerhetsöverträdelser kan förhindrasoch mitigeras i ett MQTT protokoll för att öka den överliggande säkerheten. Jag är särskiltintresserad av tekniker såsom Fuzzing, Fuzzy Logic och Machine Learning..För att undersöka syftet, analyserade och diskuterade jag tidigare implementationer avFuzzing, Fuzzy Logic och Machine Learning, i ett MQTT protokoll. Analysen visade attFuzzing ansågs vara en väldigt effektiv metod för att förhindra säkerhetsöverträdelser samtatt både Fuzzy Logic och Machine Learning var effektiva metoder för mitigering.Sammanfattningsvis, kan säkerhetsnivån i ett MQTT protokoll öka genom implementering avmetoder som används i syfte att förhindra och mitigera säkerhetsöverträdelser. Exempelviskan man först använda Fuzzing för att hitta och korrigera sårbarheter och därigenomförhindra dem. Därefter kan man antingen använda sig av Fuzzy Logic eller MachineLearning för att mitigera plötsliga attacker på MQTT protokollet när den är i produktion.Detta betyder att att utvecklaren kan kombinera metoder för att både förhindra och mitigeraöverträdelser i syfte att öka säkerhetsnivån i ett MQTT protokoll.

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  • 1380.
    Yassin, Amin
    Örebro University, School of Science and Technology.
    Program för schemadesign2018Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Database technology is becoming more actively used in different sectors and branches and the variations of database management systems (DBMS) are on the increase now more than ever. This increase is due to the strive to satisfy the variating needs to manage data. This report presents the development of a scheduling application on Windows for the School of Science and Technology at Örebro University. For this development process the WPF framework by Microsoft is used. Also, SQLite database is used to save and retrieve data in the form of resources for schedules. This report also investigates SQL- and NoSQL-databases. This investigation focuses on the differences between these two when incorporating them into Windows applications and the conclusion is that SQL-databases are to be used when the programmer is aware exactly of what types of data are needed for the application and to ensure that integrity constraints on data is a top priority. On the other hand, NoSQL-databases are suitable for applications that are a part of a distributed database system and that two of the three letters in the CAP-theorem are to be considered.

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    fulltext
  • 1381.
    Yuan, Weihao
    et al.
    Hong Kong University of Science and Technology, Hong Kong, China.
    Hang, Kaiyu
    Department of Mechanical Engineering and Material Science, Yale University, New Haven, Connecticut, USA.
    Song, Haoran
    Hong Kong University of Science and Technology, Hong Kong, China.
    Kragic, Danica
    Centre for Autonomous Systems, EECS, KTH Royal Institute of Technology, Stockholm, Sweden.
    Wang, Michael Yu
    Hong Kong University of Science and Technology, Hong Kong, China.
    Stork, Johannes Andreas
    Centre for Autonomous Systems, EECS, KTH Royal Institute of Technology, Stockholm, Sweden.
    Reinforcement Learning in Topology-based Representation for Human Body Movement with Whole Arm Manipulation2018Manuscript (preprint) (Other academic)
  • 1382.
    Yuan, Weihao
    et al.
    HKUST Robotics Institute, Hong Kong University of Science and Technology, Hong Kong.
    Stork, Johannes Andreas
    Robotics, Perception and Learning Lab, Centre for Autonomous Systems, KTH Royal Institute of Technology, Stockholm, Sweden.
    Kragic, Danica
    Wang, Michael Y.
    Hang, Kaiyu
    HKUST Robotics Institute, Hong Kong University of Science and Technology, Hong Kong.
    Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning2018In: 2018 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2018, p. 270-277Conference paper (Refereed)
    Abstract [en]

    Rearranging objects on a tabletop surface by means of nonprehensile manipulation is a task which requires skillful interaction with the physical world. Usually, this is achieved by precisely modeling physical properties of the objects, robot, and the environment for explicit planning. In contrast, as explicitly modeling the physical environment is not always feasible and involves various uncertainties, we learn a nonprehensile rearrangement strategy with deep reinforcement learning based on only visual feedback. For this, we model the task with rewards and train a deep Q-network. Our potential field-based heuristic exploration strategy reduces the amount of collisions which lead to suboptimal outcomes and we actively balance the training set to avoid bias towards poor examples. Our training process leads to quicker learning and better performance on the task as compared to uniform exploration and standard experience replay. We demonstrate empirical evidence from simulation that our method leads to a success rate of 85%, show that our system can cope with sudden changes of the environment, and compare our performance with human level performance.

  • 1383.
    Zain-ul-Abdin,
    Örebro University, School of Science and Technology.
    Programming of coarse-grained reconfigurable architectures2011Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Coarse-grained reconfigurable architectures, which offer massive parallelism coupled with the capability of undergoing run-time reconfiguration, are gaining attention in order to meet not only the increased computational demands of high-performance embedded systems, but also to fulfill the need of adaptability to functional requirements of the application. This thesis focuses on the programming aspects of such coarse-grained reconfigurable computing devices, including the relevant computation models that are capable of exposing different kinds of parallelism inherent in the application and the ability of these models to capture the adaptability requirements of the application. The thesis suggests the occam-pi language for programming of a broad class of coarse-grained reconfigurable architectures as an intermediate language; we call it intermediate, since we believe that the applicationprogramming is best done in a high-level domain-specific language. The salient properties of the occam-pi language are explicit concurrency with built-in mechanisms for interprocessorcommunication, provision for expressing dynamic parallelism, support for the expression of dynamic reconfigurations, and placement attributes. To evaluate the programming approach, a compiler framework was extended to support the language extensions in the occam-pi language, and backends were developed to target two different coarse-grained reconfigurable architectures. XPP and Ambric. The results on XPP reveal that the occam-pi based implementations produce comparable throughput to those of NML programs, while programming at a much higher level of abstraction than that of NML. Similarly the two occam-pi implementations of autofocus criterion calculation targeted to the Ambric platform outperform the CPU implementation by factors of 11-23. Thus, the results of the implemented case-studies suggest that the occam-pi language based approach simplifies the development of applications employing run-time reconfigurable devices without compromising the performance benefits.

    List of papers
    1. A Study of Design Efficiency with a High-Level Language for FPGAs
    Open this publication in new window or tab >>A Study of Design Efficiency with a High-Level Language for FPGAs
    2007 (English)In: Proceedings of the 14th International Reconfigurable Architectures Workshop (RAW'07), Piscataway, N.J.: IEEE , 2007, p. 1-7Conference paper, Published paper (Refereed)
    Abstract [en]

    Over the years reconfigurable computing devices such as FPGAs have evolved from gate-level glue logic to complex reprogrammable processing architectures. However, the tools used for mapping computations to such architectures still require the knowledge about architectural details of the target device to extract efficiency. A study of the Mobius language and tools is presented in this paper, with a focus on generated hardware performance. A number of streaming and memory-intensive applications have been developed and the results have been compared with the corresponding implementations in VHDL and a behavioral hardware description language. Based upon experimental evidences, it is concluded that Mobius, a minimal parallel processing language targeted for reconfigurable architectures, enhances productivity in terms of design time and code maintainability without considerably compromising performance and resources.

    Place, publisher, year, edition, pages
    Piscataway, N.J.: IEEE, 2007
    Keywords
    FPGA, Mobius language, VHDL, behavioral hardware description language, high-level language, minimal parallel processing language, reconfigurable computing device, eprogrammable processing architecture
    National Category
    Computer Sciences
    Research subject
    Computer and Systems Science
    Identifiers
    urn:nbn:se:oru:diva-15259 (URN)10.1109/IPDPS.2007.370394 (DOI)2-s2.0-34548787179 (Scopus ID)2082/2363 (Local ID)1424409101 (ISBN)2082/2363 (Archive number)2082/2363 (OAI)
    Conference
    IEEE International Parallel and Distributed Processing Symposium, 2007. IPDPS 2007
    Note

    ©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    Available from: 2011-04-14 Created: 2011-04-14 Last updated: 2023-05-10Bibliographically approved
    2. Evolution in architectures and programming methodologies of coarse-grained reconfigurable computing
    Open this publication in new window or tab >>Evolution in architectures and programming methodologies of coarse-grained reconfigurable computing
    2009 (English)In: Microprocessors and microsystems, ISSN 0141-9331, E-ISSN 1872-9436, Vol. 33, no 3, p. 161-178Article in journal (Refereed) Published
    Abstract [en]

    In order to meet the increased computational demands of, e.g., multimedia applications, such as video processing in HDTV, and communication applications, such as baseband processing in telecommunication systems, the architectures of reconfigurable devices have evolved to coarse-grained compositions of functional units or program controlled processors, which are operated in a coordinated manner to improve performance and energy efficiency.

    In this survey we explore the field of coarse-grained reconfigurable computing on the basis of the hardware aspects of granularity, reconfigurability, and interconnection networks, and discuss the effects of these on energy related properties and scalability. We also consider the computation models that are being adopted for programming of such machines, models that expose the parallelism inherent in the application in order to achieve better performance. We classify the coarse-grained reconfigurable architectures into four categories and present some of the existing examples of these categories. Finally, we identify the emerging trends of introduction of asynchronous techniques at the architectural level and the use of nano-electronics from technological perspective in the reconfigurable computing discipline.

    Place, publisher, year, edition, pages
    Amsterdam: Elsvier, 2009
    Keywords
    Reconfigurable architectures, Coarse-grained arrays, Computation models, Globally-asynchronous locally-synchronous
    National Category
    Engineering and Technology Computer Sciences
    Research subject
    Computer and Systems Science
    Identifiers
    urn:nbn:se:oru:diva-15260 (URN)10.1016/j.micpro.2008.10.003 (DOI)000266230500001 ()2-s2.0-66349084712 (Scopus ID)
    Projects
    Embedded Parallel Computing
    Available from: 2009-09-18 Created: 2011-04-14 Last updated: 2023-12-08Bibliographically approved
    3. Using a CSP based programming model for reconfigurable processor arrays
    Open this publication in new window or tab >>Using a CSP based programming model for reconfigurable processor arrays
    2008 (English)In: Prodeedings of International Conference on Reconfigurable Computing and FPGAs, 2008. ReConFig '08, Los Alamitos, California: IEEE Computer Society , 2008, p. 343-348Conference paper, Published paper (Refereed)
    Abstract [en]

    The growing trend towards adoption of flexible and heterogeneous, parallel computing architectures has increased the challenges faced by the programming community. We propose a method to program an emerging class of reconfigurable processor arrays by using the CSP based programming model of occam-pi. The paper describes the extension of an existing compiler platform to target such architectures. To evaluate the performance of the generated code, we present three implementations of the DCT algorithm. It is concluded that CSP appears to be a suitable computation model for programming a wide variety of reconfigurable architectures.

    Place, publisher, year, edition, pages
    Los Alamitos, California: IEEE Computer Society, 2008
    Keywords
    CSP, Programming Models, Coarse-grained Reconfigurable Architectures
    National Category
    Computer Engineering Engineering and Technology
    Research subject
    Computer Technology
    Identifiers
    urn:nbn:se:oru:diva-15261 (URN)10.1109/ReConFig.2008.41 (DOI)2-s2.0-62349104086 (Scopus ID)9780769534749 (ISBN)
    Conference
    2008 International Conference on Reconfigurable Computing and FPGAs, ReConFig 2008, 3-5 December 2008, Cancun, Mexico
    Note

    ©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    Available from: 2011-04-14 Created: 2011-04-14 Last updated: 2023-05-10Bibliographically approved
    4. Specifying Run-time Reconfiguration in Processor Arrays using High-level language
    Open this publication in new window or tab >>Specifying Run-time Reconfiguration in Processor Arrays using High-level language
    2010 (English)In: WRC 2010: 4th HiPEAC Workshop on Reconfigurable Computing, Pisa, 2010, p. 1-10Conference paper, Published paper (Refereed)
    Abstract [en]

    The adoption of run-time reconfigurable parallel architectures for high-performance embedded systems is constrained by the lackof a unified programming model which can express both parallelism and reconfigurability. We propose to program an emerging class of reconfigurable processor arrays by using the programming model of occam-pi and describe how the extensions of channel direction specifiers, mobile data, dynamic process invocation, and process placement attributes can be used to express run-time reconfiguration in occam-pi. We present implementations of DCT algorithm to demonstrate the applicability of occam-pi to express reconfigurability. We concluded that occam-pi appears to be a suitable programming model for programming run-time reconfigurable processor arrays.

    Place, publisher, year, edition, pages
    Pisa: , 2010
    National Category
    Computer Sciences Engineering and Technology
    Research subject
    Computer and Systems Science
    Identifiers
    urn:nbn:se:oru:diva-15262 (URN)
    Conference
    HiPEAC Workshop on Reconfigurable Computing
    Available from: 2011-04-14 Created: 2011-04-14 Last updated: 2023-01-17Bibliographically approved
    5. Programming real-time autofocus on a massively parallel reconfigurable architecture using Occam-pi
    Open this publication in new window or tab >>Programming real-time autofocus on a massively parallel reconfigurable architecture using Occam-pi
    2011 (English)In: Proceedings of the 19th Annual IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM 2011), IEEE conference proceedings, 2011, p. 194-201Conference paper, Published paper (Other academic)
    Abstract [en]

    Recently we proposed occam-pi as a high-level language for programming massively parallel reconfigurable architectures. The design of occam-pi incorporates ideas from CSP and pi-calculus to facilitate expressing parallelism and reconfigurability. The feasability of this approach was illustratedby building three occam-pi implementations of DCT executing on an Ambric. However, because DCT is a simple and well studied algorithm it remained uncertain whether occam-pi would also be effective for programming novel, more complex algorithms.

    In this paper, we demonstrate the applicability of occam-pi for expressing various degrees of parallelism by implementinga significantly large case-study of focus criterion calculation inan autofocus algorithm on the Ambric architecture. Autofocus is a key component of synthetic aperture radar systems. Two implementations of focus criterion calculation were developedand evaluated on the basis of performance. The comparison of the performance results with a single threaded software implementation of the same algorithm show that the throughput of the two implementations are 11x and 23x higher than the sequential implementation despite a much lower (9x) clock frequency. The two designs are, respectively, 29x and 40x moreenergy efficient.

    Place, publisher, year, edition, pages
    IEEE conference proceedings, 2011
    National Category
    Engineering and Technology Computer Sciences
    Research subject
    Computer and Systems Science
    Identifiers
    urn:nbn:se:oru:diva-15263 (URN)10.1109/FCCM.2011.20 (DOI)000298664800034 ()
    Conference
    19th Annual IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM 2011), Salt Lake City, Utah, USA, May 1-3
    Projects
    SMECY
    Available from: 2011-03-22 Created: 2011-04-14 Last updated: 2023-05-10Bibliographically approved
    6. Occam-pi as a high-level language for coarse-grained reconfigurable architectures
    Open this publication in new window or tab >>Occam-pi as a high-level language for coarse-grained reconfigurable architectures
    2011 (English)In: 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), IEEE conference proceedings, 2011, p. 236-243Conference paper, Published paper (Refereed)
    Abstract [en]

    Recently we proposed occam-pi as a high-levellanguage for programming coarse grained reconfigurable architectures. The constructs of occam-pi combine ideas from CSPand pi-calculus to facilitate expressing parallelism, communication, and reconfigurability. The feasability of this approachwas illustrated by developing a compiler framework to compile occam-pi implementations to the Ambric architecture.

    In this paper, we demonstrate the applicability of occam-pif or programing an array of functional units, eXtreme ProcessingPlatform (XPP). This is made possible by extending the compilerframework to target the XPP architecture, including automatic floating to fixed-point conversion. Different implementations of a FIR filter and a DCT algorithm were developed and evaluated on the basis of performance and resource consumption. The reported results reveal that the approach of using occam-pito program the category of coarse grained reconfigurable architectures appears to be promising. The resulting implementations are generally much superior to those programmed in C and comparable to those hand-coded in the low-level native language NML.

    Place, publisher, year, edition, pages
    IEEE conference proceedings, 2011
    National Category
    Computer Engineering Engineering and Technology
    Research subject
    Computer Technology
    Identifiers
    urn:nbn:se:oru:diva-15264 (URN)10.1109/IPDPS.2011.147 (DOI)9781612844251 (ISBN)
    Conference
    Reconfigurable Architectures Workshop (RAW'2011) in conjunction with International Parallel and Distributed Processing Symposium (IPDPS'2011), Shanghai, 16-20 May 2011
    Available from: 2011-03-22 Created: 2011-04-14 Last updated: 2023-05-10Bibliographically approved
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    SPIKBLAD01
  • 1384.
    Zain-ul-Abdin,
    et al.
    Örebro University, School of Science and Technology.
    Ahlander, Anders
    Svensson, Bertil
    Högskolan i Halmstad, Halmstad, Sweden.
    Programming real-time autofocus on a massively parallel reconfigurable architecture using Occam-pi2011In: Proceedings of the 19th Annual IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM 2011), IEEE conference proceedings, 2011, p. 194-201Conference paper (Other academic)
    Abstract [en]

    Recently we proposed occam-pi as a high-level language for programming massively parallel reconfigurable architectures. The design of occam-pi incorporates ideas from CSP and pi-calculus to facilitate expressing parallelism and reconfigurability. The feasability of this approach was illustratedby building three occam-pi implementations of DCT executing on an Ambric. However, because DCT is a simple and well studied algorithm it remained uncertain whether occam-pi would also be effective for programming novel, more complex algorithms.

    In this paper, we demonstrate the applicability of occam-pi for expressing various degrees of parallelism by implementinga significantly large case-study of focus criterion calculation inan autofocus algorithm on the Ambric architecture. Autofocus is a key component of synthetic aperture radar systems. Two implementations of focus criterion calculation were developedand evaluated on the basis of performance. The comparison of the performance results with a single threaded software implementation of the same algorithm show that the throughput of the two implementations are 11x and 23x higher than the sequential implementation despite a much lower (9x) clock frequency. The two designs are, respectively, 29x and 40x moreenergy efficient.

  • 1385.
    Zain-ul-Abdin,
    et al.
    Örebro University, School of Science and Technology.
    Svensson, Bertil
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
    A Study of Design Efficiency with a High-Level Language for FPGAs2007In: Proceedings of the 14th International Reconfigurable Architectures Workshop (RAW'07), Piscataway, N.J.: IEEE , 2007, p. 1-7Conference paper (Refereed)
    Abstract [en]

    Over the years reconfigurable computing devices such as FPGAs have evolved from gate-level glue logic to complex reprogrammable processing architectures. However, the tools used for mapping computations to such architectures still require the knowledge about architectural details of the target device to extract efficiency. A study of the Mobius language and tools is presented in this paper, with a focus on generated hardware performance. A number of streaming and memory-intensive applications have been developed and the results have been compared with the corresponding implementations in VHDL and a behavioral hardware description language. Based upon experimental evidences, it is concluded that Mobius, a minimal parallel processing language targeted for reconfigurable architectures, enhances productivity in terms of design time and code maintainability without considerably compromising performance and resources.

    Download full text (pdf)
    fulltext
  • 1386.
    Zain-ul-Abdin,
    et al.
    Örebro University, School of Science and Technology.
    Svensson, Bertil
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Embedded Systems (CERES).
    Evolution in architectures and programming methodologies of coarse-grained reconfigurable computing2009In: Microprocessors and microsystems, ISSN 0141-9331, E-ISSN 1872-9436, Vol. 33, no 3, p. 161-178Article in journal (Refereed)
    Abstract [en]

    In order to meet the increased computational demands of, e.g., multimedia applications, such as video processing in HDTV, and communication applications, such as baseband processing in telecommunication systems, the architectures of reconfigurable devices have evolved to coarse-grained compositions of functional units or program controlled processors, which are operated in a coordinated manner to improve performance and energy efficiency.

    In this survey we explore the field of coarse-grained reconfigurable computing on the basis of the hardware aspects of granularity, reconfigurability, and interconnection networks, and discuss the effects of these on energy related properties and scalability. We also consider the computation models that are being adopted for programming of such machines, models that expose the parallelism inherent in the application in order to achieve better performance. We classify the coarse-grained reconfigurable architectures into four categories and present some of the existing examples of these categories. Finally, we identify the emerging trends of introduction of asynchronous techniques at the architectural level and the use of nano-electronics from technological perspective in the reconfigurable computing discipline.

  • 1387.
    Zain-ul-Abdin,
    et al.
    Akademin för naturvetenskap och teknik, School of Science and Technology *(2012-01-16), Örebro University, Örebro, Sweden.
    Svensson, Bertil
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Embedded Systems (CERES). Halmstad University, Halmstad, Sweden.
    Specifying Run-time Reconfiguration in Processor Arrays using High-level language2010In: WRC 2010: 4th HiPEAC Workshop on Reconfigurable Computing, Pisa, 2010, p. 1-10Conference paper (Refereed)
    Abstract [en]

    The adoption of run-time reconfigurable parallel architectures for high-performance embedded systems is constrained by the lackof a unified programming model which can express both parallelism and reconfigurability. We propose to program an emerging class of reconfigurable processor arrays by using the programming model of occam-pi and describe how the extensions of channel direction specifiers, mobile data, dynamic process invocation, and process placement attributes can be used to express run-time reconfiguration in occam-pi. We present implementations of DCT algorithm to demonstrate the applicability of occam-pi to express reconfigurability. We concluded that occam-pi appears to be a suitable programming model for programming run-time reconfigurable processor arrays.

    Download full text (pdf)
    fulltext
  • 1388.
    Zhang, Liwei
    et al.
    University of Hamburg, Hamburg, Germany.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Zhang, Jianwei
    University of Hamburg, Hamburg, Germany.
    Evaluation metrics for an experience-based mobile artificial cognitive system2014In: 11th World Congress on Intelligent Control and Automation (WCICA2014), Springer Berlin/Heidelberg, 2014, p. 2225-2232Conference paper (Refereed)
    Abstract [en]

    In this paper, an FIM (Fitness to Ideal Model)and a DLen (Description Length) based evaluation approachhas been developed to measure the benefit of learning from experienceto improve the robustness of the robot’s behavior. Theexperience based mobile artificial cognitive system architectureis briefly described and adopted by a PR2 service robot withinthe EU-FP7 funded project RACE. The robot conducts typicaltasks of a waiter. Temporal and lasting obstacles and standardtable items, as shown in the demonstrations of “Deal-withobstacles”and “Clear-table-intelligently”, are being adoptedin this work to test the proposed evaluation metrics, validateit on a real PR2 robot system and present the evaluationresults. The relationship between the FIM and DLen has beenvalidated. This work proposes an effective approach to evaluatea cognitive service robot system which enhances its performanceby learning.

  • 1389.
    Zhang, Yue
    et al.
    MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application, East China Normal University, Shanghai, China; National Trusted Embedded Software Engineering Technology Research Center (No. 2012FU125X15), Shanghai, China.
    Dragoni, Nicola
    Örebro University, School of Science and Technology. DTU Compute, Technical University of Denmark, Richard Petersens Plads, Kongens Lyngby, Denmark.
    Wang, Jiangtao
    MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application, East China Normal University, Shanghai, China; National Trusted Embedded Software Engineering Technology Research Center (No. 2012FU125X15), Shanghai, China.
    A Framework and Classification for Fault Detection Approaches in Wireless Sensor Networks with an Energy Efficiency Perspective2015In: International Journal of Distributed Sensor Networks, ISSN 1550-1329, E-ISSN 1550-1477, article id 678029Article, review/survey (Refereed)
    Abstract [en]

    Wireless Sensor Networks (WSNs) are more and more considered a key enabling technology for the realisation of the Internet of Things (IoT) vision. With the long term goal of designing fault-tolerant IoT systems, this paper proposes a fault detection framework for WSNs with the perspective of energy efficiency to facilitate the design of fault detection methods and the evaluation of their energy efficiency. Following the same design principle of the fault detection framework, the paper proposes a classification for fault detection approaches. The classification is applied to a number of fault detection approaches for the comparison of several characteristics, namely, energy efficiency, correlation model, evaluation method, and detection accuracy. The design guidelines given in this paper aim at providing an insight into better design of energy-efficient detection approaches in resource-constraint WSNs.

  • 1390.
    Zhu, Yufei
    et al.
    Örebro University, School of Science and Technology.
    Rudenko, Andrey
    Bosch Corporate Research, Robert Bosch GmbH, Stuttgart, Germany.
    Kucner, Tomasz
    Finnish Center for Artificial Intelligence, School of Electrical Engineering, Aalto University, Finland.
    Palmieri, Luigi
    Bosch Corporate Research, Robert Bosch GmbH, Stuttgart, Germany.
    Arras, Kai
    Bosch Corporate Research, Robert Bosch GmbH, Stuttgart, Germany.
    Lilienthal, Achim
    Örebro University, School of Science and Technology. TU Munich, Germany.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    CLiFF-LHMP: Using Spatial Dynamics Patterns for Long-Term Human Motion Prediction2023In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 01-05 October 2023, Detroit, MI, USA, IEEE, 2023, p. 3795-3802Conference paper (Refereed)
    Abstract [en]

    Human motion prediction is important for mobile service robots and intelligent vehicles to operate safely and smoothly around people. The more accurate predictions are, particularly over extended periods of time, the better a system can, e.g., assess collision risks and plan ahead. In this paper, we propose to exploit maps of dynamics (MoDs, a class of general representations of place-dependent spatial motion patterns, learned from prior observations) for long-term human motion prediction (LHMP). We present a new MoD-informed human motion prediction approach, named CLiFF-LHMP, which is data efficient, explainable, and insensitive to errors from an upstream tracking system. Our approach uses CLiFF -map, a specific MoD trained with human motion data recorded in the same environment. We bias a constant velocity prediction with samples from the CLiFF-map to generate multi-modal trajectory predictions. In two public datasets we show that this algorithm outperforms the state of the art for predictions over very extended periods of time, achieving 45 % more accurate prediction performance at 50s compared to the baseline.

    Download full text (pdf)
    CLiFF-LHMP: Using Spatial Dynamics Patterns for Long-Term Human Motion Prediction
  • 1391.
    Åkerberg, Johan
    et al.
    ABB Corporate Research, Västerås, Sweden.
    Gidlund, Mikael
    ABB Corporate Research, Västerås, Sweden.
    Lennvall, Tomas
    ABB Corporate Research, Västerås, Sweden.
    Neander, Jonas
    ABB Corporate Research, Sweden.
    Björkman, Mats
    School of Innovation, Design, and Technology, Mälardalen University, Västerås, Sweden.
    Integration of WirelessHART Networks in Distributed Control Systems using PROFINET IO2010In: 8th IEEE International Conference on Industrial Informatics (INDIN), Institute of Electrical and Electronics Engineers (IEEE), 2010, p. 154-159Conference paper (Refereed)
    Abstract [en]

    In this paper we describe a method to integrate WirelessHART networks in Distributed Control Systems (DCS)using PROFINET IO. By modeling the WirelessHART network in the Generic Station Description file, that describes a PROFINET IO device, the WirelessHART related configuration can be distributed from the central engineering stations. In this way, both process controller configuration and WirelessHART network configuration is engineered and maintained from a central location. Thus the end-user do not need any additional tool-specific training, as the existing tools are used to engineer the WirelessHART networks. We base the method of integration on the keywords simple deployment and maintenance, and flexible topology. A proof-of-concept implementation of the proposed method shows that it is possible to download WirelessHART configuration both to the WirelessHART network managers, as well as the WirelessHART sensors. By integrating WirelessHART in this way, maintenance is greatly simplified as the actual configuration will be downloaded automatically by the DCS when faulty field devices are replaced.

  • 1392.
    Åkerberg, Johan
    et al.
    ABB Corporate Research, Västerås, Sweden.
    Gidlund, Mikael
    ABB Corporate Research, Västerås, Sweden.
    Neander, Jonas
    ABB Corporate Research, Västerås, Sweden.
    Lennvall, Tomas
    ABB Corporate Research, Västerås, Sweden.
    Björkman, Mats
    School of Innovation, Design, and Technology, Mälardalens University, Västerås, Sweden.
    Deterministic Downlink Transmission in WirelessHART Networks enabling Wireless Control Applications2010In: IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society, New York: Institute of Electrical and Electronics Engineers (IEEE), 2010, p. 2120-2125Conference paper (Refereed)
    Abstract [en]

    Wireless sensor and actuator networks bring many benefits to industrial automation systems. However, unreliable wireless and multihop communications among sensors and actuators cause challenges in designing such systems. Wireless HART is the first standard for wireless real-time industrial applications. However, current Wireless HART standard does not provide services for efficient usage of actuators, which are an essential part of automation. In this paper we focus on Wireless HART and propose a periodic and deterministic downlink transmission functionality which enables efficient usage of actuators and control applications. Furthermore, we define new HART commands extending the interface, without affecting available services, to support the integration of actuators. This can be achieved with minor changes in the current standard.

  • 1393.
    Öjebo, Erik
    Örebro University, School of Science and Technology.
    Objekt-relationsmappning i datacentrerad applikation2009Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    This report presents a study of six different object-relational mapping frameworks, namely Entity Framework, LINQ to SQL, NHibernate, Castle ActiveRecord, MyGeneration Doodads and Subsonic. The study describes the strengths and weaknesses of the various frameworks and discusses when each framework is appropriate to use.

    The frameworks that were judged to be the most interesting were NHibernate and Entity Framework, since they provide flexible mapping between the domain model and the underlying database schema as well as good availability of documentation and literature.

    The study was used as a basis for deciding which of the frameworks that should be used in a rewrite of an existing application for the IT consulting company Sogeti. The framework that was considered the most appropriate for the application was NHibernate.

    Download full text (pdf)
    orm_i_datacentrerad_applikation
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