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  • 1.
    Appelberg, John
    et al.
    Jönköping University, School of Engineering, JTH, Department of Computer Science and Informatics.
    Andersson, Adam
    Jönköping University, School of Engineering, JTH, Department of Computer Science and Informatics.
    Improving the diagnostic process for robotic lawnmowers: After-sales efficiency benefits from an Experimental Diagnostic Tool2022Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    An Experimental Diagnostic Tool (EDT) was developed to increase the efficiency of the diagnostic process of robotic lawn mowers which resulted in a 200% productivity increase when utilizing a proposed formula specifically designed for the diagnostic process of robotic lawn mowers. The tool does not only extract and process data from the robotic lawn mower wirelessly but also highlights potential faults through an intuitive and easy-to-use interface - empowering servicing technicians who perform the diagnostics to perform at a higher level.

    Further, efficiency is a term widely used in various domains and contexts when measuring the capacity of a process. Proposed general definitions of the term have been given by previous authors and researchers. However, due to a lack of universally set definitions which fit all situations, the term remains ambiguous when improvements to a specific process are needed. The unclear definition is due to the variations within each process affecting the definition of both the term itself, but also similar terms fundamentally connected to it such as productivity, performance, and profitability. The following report contains an investigation of exploratory research where the understanding of efficiency and its related concepts are analyzed within the after-sales diagnostic process of robotic lawn mowers.

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  • 2.
    Ashour Pour, Milad
    et al.
    Jönköping University, School of Engineering, JTH, Industrial Product Development, Production and Design.
    Johansen, Kerstin
    Jönköping University, School of Engineering, JTH, Industrial Product Development, Production and Design, JTH, Production development.
    Deployment of additive manufacturing and robotics for increasing flexibility in productions2022In: SPS2022: Proceedings of the 10th Swedish production symposium / [ed] A. H. C. Ng, A. Syberfelt, D. Högberg & M. Holm, Amsterdam: IOS Press, 2022, p. 533-541Conference paper (Refereed)
    Abstract [en]

    As manufacturing industry seeks different strategies and technologies to respond to the ever-increasing demands in markets that prioritize versatility of products with low-volume productions, certain technologies and strategies gain more attraction and form higher acceptance levels among different sectors. Individual firms are driven by their market requirements. Various factors including product specification, assembly sequence, and manufacturing operations are central to the decisions that are made with respect to the type of technology to respond to market dynamics. Additive Manufacturing (AM) is one of the technology alternatives that has exhibited remarkable strengths in countering market disruptions. Although AM can be utilized along conventional technologies (i.e., subtracting and forming) in a hybrid context to combine advantages and offset weaknesses of each category, the arguments supporting its applications would need to be formulated rigorously to ensure investments are rightfully justified. Another alternative continuously investigated by companies is automation and more specifically, using robotics for various purposes e.g., operations like welding and painting, material handling, machine tending, etc. Both industrial robots and the applications that require a collaboration between humans and robots can be valid in this context. Considering advancements in AM and Automation and their potentials in increasing flexibility, expediting operations, and leveraging cost advantages, this paper explores how AM and automation in tandem could improve flexibility in productions. Results of this study can be used for proposing a conceptual model which will be further developed and then tested on industrial cases in future studies. While this study incorporates raw data about processing requirements in production that has been obtained via interviews with industrial companies, inputs about the technologies i.e., AM and robotics are derived from literature.

  • 3.
    Bach, Willy
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Vidarsson, Petter
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Integrering av en robotgräsklippare i en 3-dimensionell simulering.2019Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [sv]

    I takt med att marknaden för robotgräsklippare ökar så är pressen högre på företag att deras produkt ska vara robust. Detta kan uppnås genom att testerna som görs på robotgräsklipparen testas så fort som möjligt. Genom att skapa en simulering där alla tester genomförs istället för att köra testerna på en fysisk robotgräsklippare kan detta uppnås och utifrån detta utformades forskningsfrågan. För att skapa simuleringen undersöktes först mjukvaror vilket ansågs lämpliga för att utveckla en simulator, detta gjordes via en fallstudie. Dessa har sedan analyserats och jämförts för att till slut bestämma de som ansetts bäst att använda. Med hjälp av de så startade en utveckling av en simulator där hjul- och kollisionsdata hämtades från en fysisk robotgräsklippare och skickades till en virtuell robotgräsklippare. Den färdigställda simulatorn utvärderades vid slutet av arbetet med hjälp av experiment där författarna observerade och jämförde rörelsen hos den fysiska och virtuella robotgräsklipparen. För att utföra denna uppgift så tillämpade arbetet metoden Design science research där det arbetades iterativt vid utvecklingen av simulatorn. Resultatet visar på att det är möjligt att skapa en simulator med de valda mjukvarorna ROS och Gazebo där man kan genomföra simulerade tester. Arbetet visar på ökad kunskap där data från en fysisk robotgräsklippare kan implementeras i 3D-simulatorn Gazebo via ramverket ROS. Studien kan användas som riktlinje i liknande projekt när det kommer till val av mjukvaror och om de är lämpliga. Arbetet begränsas till enbart hjul- och kollisionsdata från den fysiska robotgräsklipparen.

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  • 4.
    Bergenholtz, Claes
    et al.
    Jönköping University, School of Engineering, JTH, Department of Computing. Jönköping University, School of Engineering, JTH, Department of Computer Science and Informatics.
    Isacsson, John
    Jönköping University, School of Engineering, JTH, Department of Computing. Jönköping University, School of Engineering, JTH, Department of Computer Science and Informatics.
    Evaluation of a robotic testing dashboard (RTD) used to compare autonomous robots with human pilots2021Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Autonomous robots are becoming a bigger part of our society. This thesis aims to evaluate a robot testing dashboard (RTD) that can be used as a new way of finding improvements when developing autonomous robots that do not use machine learning.

    The method that is used is design science research, which is used when creating and evaluating an artifact to address a practical problem. In our case the artifact isthe RTD.

    This project was performed at a company called Greenworks, which among other things develops and sells autonomous lawn mowers. The company wants to find new testing methods to help develop their autonomous lawnmowers. The RTD is created to visualize the inputs that the lawn mower utilizes to perform its tasks. A human pilot will then control the lawn mower, by only looking at that visualized data. If the pilot using the RTD can execute the same tasks as the lawn mower in its autonomous mode, the test results can be analyzed to see whether the human has done some parts of the tasks differently. The best outcome from the analysis of the test results is to find areas of improvement that can be implemented into the autonomous lawn mower design, both in software and hardware.

    For this purpose, an RTD was built and tested at Greenworks. From the tests using the RTD we concluded that it is helpful in the testing process, and we could find areas of improvements after analysis of our tests. However, the use of the RTD will require more time and resources compared to other methods. Each company that uses a similar dashboard concept will have to evaluate if the benefits are worth the time. Furthermore, the concept may not suit all areas of robotics but does seem to suit situations where a human can have an advantage over robots, such as in creative problem solving.

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  • 5.
    Botteghi, N.
    et al.
    University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science, Enschede, Netherlands.
    Alaa, K.
    IAV GmbH (Volkswagen Group), Intelligent Driving Functions RD Center, Berlin, Germany.
    Poel, M.
    University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science, Enschede, Netherlands.
    Sirmacek, Beril
    Jönköping University, School of Engineering, JTH, Department of Computer Science and Informatics. Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Brune, C.
    University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science, Enschede, Netherlands.
    Mersha, A.
    Research Group of Mechatronics, Saxion University of Applied Sciences, Enschede, Netherlands.
    Stramigioli, S.
    University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science, Enschede, Netherlands.
    Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces2021In: IEEE International Conference on Intelligent Robots and Systems, Institute of Electrical and Electronics Engineers (IEEE), 2021, p. 190-197Conference paper (Refereed)
    Abstract [en]

    Reinforcement learning algorithms have proven to be capable of solving complicated robotics tasks in an end-to-end fashion without any need for hand-crafted features or policies. Especially in the context of robotics, in which the cost of real-world data is usually extremely high, Reinforcement Learning solutions achieving high sample efficiency are needed. In this paper, we propose a framework combining the learning of a low-dimensional state representation, from high-dimensional observations coming from the robot's raw sensory readings, with the learning of the optimal policy, given the learned state representation. We evaluate our framework in the context of mobile robot navigation in the case of continuous state and action spaces. Moreover, we study the problem of transferring what learned in the simulated virtual environment to the real robot without further retraining using real-world data in the presence of visual and depth distractors, such as lighting changes and moving obstacles. A video of our experiments can be found at: https://youtu.be/rUdGPKr2Wuo.

  • 6.
    Botteghi, N.
    et al.
    Robotics and Mechatronics, Faculty of Electrical Engineering Mathematics and Computer Science, University of Twente, Netherlands.
    Kamilaris, A.
    Research Centre on Interactive Media Smart Systems and Emerging Technologies, Nicosia, Cyprus.
    Sinai, L.
    Robotics and Mechatronics, Faculty of Electrical Engineering Mathematics and Computer Science, University of Twente, Netherlands.
    Sirmacek, Beril
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Multi-Agent Path Planning of Robotic Swarms in Agricultural Fields2020In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences / [ed] N. Paparoditis, C. Mallet, F. Lafarge, S. Hinz, R. Feitosa, M. Weinmann, B. Jutzi, Copernicus GmbH , 2020, Vol. 5, no 1, p. 361-368Conference paper (Refereed)
    Abstract [en]

    Collaborative swarms of robots/UAVs constitute a promising solution for precision agriculture and for automatizing agricultural processes. Since agricultural fields have complex topologies and different constraints, the problem of optimized path routing of these swarms is important to be tackled. Hence, this paper deals with the problem of optimizing path routing for a swarm of ground robots and UAVs in different popular topologies of agricultural fields. Four algorithms (Nearest Neighbour based on K-means clustering, Christofides, Ant Colony Optimisation and Bellman-Held-Karp) are applied on various farm types commonly found around Europe. The results indicate that the problem of path planning and the corresponding algorithm to use, are sensitive to the field topology and to the number of agents in the swarm.

  • 7.
    Botteghi, N.
    et al.
    Robotics and Mechatronics University of Twente, Enschede, Netherlands.
    Obbink, R.
    Robotics and Mechatronics University of Twente, Enschede, Netherlands.
    Geijs, D.
    Robotics and Mechatronics University of Twente, Enschede, Netherlands.
    Poel, M.
    Datamanagement and Biometrics, University of Twente, Enschede, Netherlands.
    Sirmacek, Beril
    Jönköping University, School of Engineering, JTH, Department of Computer Science and Informatics. Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Brune, C.
    Applied Analysis University of Twente, Enschede, Netherlands.
    Mersha, A.
    Research Group of Mechatronics, Saxion University of Applied Sciences, Enschede, Netherlands.
    Stramigioli, S.
    Robotics and Mechatronics University of Twente, Enschede, Netherlands.
    Low dimensional state representation learning with reward-shaped priors2020In: Proceedings - International Conference on Pattern Recognition, Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 3736-3743Conference paper (Refereed)
    Abstract [en]

    Reinforcement Learning has been able to solve many complicated robotics tasks without any need for feature engineering in an end-to-end fashion. However, learning the optimal policy directly from the sensory inputs, i.e the observations, often requires processing and storage of a huge amount of data. In the context of robotics, the cost of data from real robotics hardware is usually very high, thus solutions that achieve high sample-efficiency are needed. We propose a method that aims at learning a mapping from the observations into a lower-dimensional state space. This mapping is learned with unsupervised learning using loss functions shaped to incorporate prior knowledge of the environment and the task. Using the samples from the state space, the optimal policy is quickly and efficiently learned. We test the method on several mobile robot navigation tasks in a simulation environment and also on a real robot. A video of our experiments can be found at: https://youtu.be/dgWxmfSv95U.

  • 8.
    Botteghi, N.
    et al.
    Robotics and Mechatronics, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Netherlands.
    Sirmacek, Beril
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Schulte, R.
    Robotics and Mechatronics, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Netherlands.
    Poel, M.
    Datamanagement and Biometrics, Faculty of Electric Engineering, Mathematics and Computer Science, University of Twente, Netherlands.
    Brune, C.
    Applied Mathematics, Faculty of Electric Engineering, Mathematics and Computer Science, University of Twente, Netherlands.
    Reinforcement learning helps slam: Learning to build maps2020In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives / [ed] N. Paparoditis, C. Mallet, F. Lafarge, S. Zlatanova, S. Dragicevic, G. Sithole, G. Agugiaro, J. J. Arsanjani, P. Boguslawski, M. Breunig, M. A. Brovelli, S. Christophe, A. Coltekin, M. R. Delavar, M. Al Doori, E. Guilbert, C. C. Fonte, J. Haworth, U. Isikdag, I. Ivanova, Z. Kang, K. Khoshelham, M. Koeva, M. Kokla, Y. Liu, M. Madden, M. A. Mostafavi, G. Navratil, D. R. Paudyal, C. Pettit, A. Spanò, E. Stefanakis, W. Tu, G. Vacca, L. Díaz-Vilariño, S. Wise, H. Wu, and X. G. Zhou, International Society for Photogrammetry and Remote Sensing , 2020, Vol. 43, no B4, p. 329-336Conference paper (Refereed)
    Abstract [en]

    In this research, we investigate the use of Reinforcement Learning (RL) for an effective and robust solution for exploring unknown and indoor environments and reconstructing their maps. We benefit from a Simultaneous Localization and Mapping (SLAM) algorithm for real-time robot localization and mapping. Three different reward functions are compared and tested in different environments with growing complexity. The performances of the three different RL-based path planners are assessed not only on the training environments, but also on an a priori unseen environment to test the generalization properties of the policies. The results indicate that RL-based planners trained to maximize the coverage of the map are able to consistently explore and construct the maps of different indoor environments.

  • 9.
    Dhanabalachandran, Kaviya
    et al.
    Institute of Artificial Intelligence, Bremen University, Germany.
    Hassouna, Vanessa
    Institute of Artificial Intelligence Bremen University, Germany.
    Hedblom, Maria M.
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Kümpel, Michaela
    Institute of Artificial Intelligence Bremen University, Germany.
    Leusmann, Nils
    Institute of Artificial Intelligence Bremen University, Germany.
    Beetz, Michael
    Institute of Artificial Intelligence Bremen University, Germany.
    Cutting Events: Towards Autonomous Plan Adaption by Robotic Agents through Image-Schematic Event Segmentation2021In: Proceedings of the 11th on Knowledge Capture Conference, ACM Digital Library, 2021, p. 25-32Conference paper (Refereed)
    Abstract [en]

    Autonomous robots struggle with plan adaption in uncertain and changing environments. Although modern robots can make popcorn and pancakes, they are incapable of performing such tasks in unknown settings and unable to adapt action plans if ingredients or tools are missing. Humans are continuously aware of their surroundings. For robotic agents, real-time state updating is time-consuming and other methods for failure handling are required. Taking inspiration from human cognition, we propose a plan adaption method based on event segmentation of the image-schematic states of subtasks within action descriptors. For this, we reuse action plans of the robotic architecture CRAM and ontologically model the involved objects and image-schematic states of the action descriptor cutting. Our evaluation uses a robot simulation of the task of cutting bread and demonstrates that the system can reason about possible solutions to unexpected failures regarding tool use.

  • 10.
    Dhanabalachandran, Kaviya
    et al.
    Institute of Artificial Intelligence, University of Bremen, Germany.
    Hedblom, Maria M.
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Beetz, Michael
    Institute of Artificial Intelligence, University of Bremen, Germany.
    A balancing act: Ordering algorithm and image-schematic action descriptors for stacking objects by household robots2022In: Proceedings of the Joint Ontology Workshops 2022, Episode VIII: The Svear Sommar of Ontology / [ed] T. P. Sales, M. Hedblom & H. Tan, CEUR-WS , 2022Conference paper (Refereed)
    Abstract [en]

    Optimising object order in stacking problems remains a hard problem for cognitive robotics research. In this paper, we continue our work on using the spatiotemporal relationships called image schemas to represent affordance spaces founded on object properties. Based on object properties, we introduce a stacking-order algorithm and describe the action descriptors using an image-schematic event segmentation format by describing a small subset using the Image Schema Logic ISL𝐹𝑂𝐿.

  • 11.
    Dhanabalachandran, Kaviya
    et al.
    Institute of Artificial Intelligence, University of Bremen, Germany.
    Hedblom, Maria M.
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Beetz, Michael
    Institute of Artificial Intelligence, University of Bremen, Germany.
    Getting on top of things: Towards intelligent robotic object stacking through image-schematic reasoning2022In: Proceedings of the Sixth Image Schema Day 2022: Jönköping, Sweden, March 24-25th, 2022 / [ed] M. M. Hedblom & O. Kutz, CEUR-WS , 2022Conference paper (Refereed)
    Abstract [en]

    In this extended abstract, we present initial work on intelligent object stacking by household robots using a symbolic approach grounded in image schema research. Image schemas represent spatiotemporal relationships that capture objects’ affordances and dispositions. Therefore, they offer the first step to ground semantic information in symbolic descriptions. We hypothesise that for a robot to successfully stack objects of different dispositions, these relationships can be used to more intelligently identify both task constraints and relevant event segments. 

  • 12.
    Erdmann, S.
    et al.
    Rostock University, Albert-Einstein-Str. 22, Rostock, 18059, Germany.
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH, Department of Computer Science and Informatics. Rostock University, Albert-Einstein-Str. 22, Rostock, 18059, Germany.
    On Application Potential of Robotic Process Automation in Small Enterprises2022In: CEUR Workshop Proceedings: Joint of the BIR 2022 Workshops and Doctoral Consortium, BIR-WS 2022 / [ed] H. Koç, J. Stirna, K. Sandkuhl, U. Seigerroth, M. Kirikova, P. Forbrig, C. Møller, A. Gutschmidt, B. Johansson, CEUR-WS , 2022, Vol. 3223, p. 70-78Conference paper (Refereed)
    Abstract [en]

    The potential of Robotic Process Automation (RPA) for enterprises is undisputed and includes automation of routine tasks, improvement of data quality or reduction of monotonous tasks. However, our observation from projects with enterprises is that much of this potential is clearly visible in larger enterprises but not so obvious in small enterprises. SMEs often show a lower readiness to invest time and resources in new technologies. Thus, application fields requiring an investment are not exploited by SMEs. So, what is the real application potential of RPA in SMEs? Are there typical application scenarios with potentially high benefits that SMEs should focus on? This is the focus of this paper. We will address the question of application potential primarily from a qualitative perspective, i.e. the aim is not to quantify the potential but to qualify the application fields. The contributions of this paper are (1) an innovative dataspace as an example case for business model development, (2) an approach to support business model development based on data value chains and reference enterprise architectures, and (3) analysis of existing literature in the field. © 2020 Copyright for this paper by its authors.

  • 13.
    Gopinath, Varun
    et al.
    Department of Management and Engineering, Linköping University, Linköping, Sweden.
    Johansen, Kerstin
    Understanding situational and mode awareness for safe human-robot collaboration: case studies on assembly applications2019In: Production Engineering, ISSN 0944-6524, E-ISSN 1863-7353, Vol. 13, no 1, p. 1-9Article in journal (Refereed)
    Abstract [en]

    In order for humans and robots to collaborate on an assembly line, safety of operations is a prerequisite. In this article, two assembly stations where a large industrial robots collaborate with humans will be analysed with the aim to 1. determine the characteristics of hazards associated with human-robot interaction and 2. design solutions that can mitigate risks associated with these hazards. To support the aim of this article, a literature review will attempt to characterize automation and detail the problems associated with human-automation interaction. The analysis points at situational awareness and mode-awareness as contributing factors to operator and process safety. These underlying mechanisms, if recognised by the risk assessment team as hazards, can mitigate risks of operator injury or production delays. This article details the function of visual and physical interfaces that allow operators to comprehend system-state in order to avoid undesirable situations. 

  • 14.
    Hedblom, Maria M.
    et al.
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Kutz, O.Free University of Bozen-Bolzano, Italy.
    ISD6 The Image Schema Day 20222022Conference proceedings (editor) (Other academic)
  • 15.
    Hedblom, Maria M.
    et al.
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Kutz, OliverFree University of Bozen-Bolzano, Research Centre for Knowledge and Data (KRDB), Italy.
    ISD7 2023, The Seventh Image Schema Day: Proceedings of the Seventh Image Schema Day, co-located with The 20th International Conference on Principles of Knowledge Representation and Reasoning (KR 2023)2023Conference proceedings (editor) (Refereed)
  • 16.
    Hedblom, Maria M.
    et al.
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Kutz, OliverFree University of Bozen-Bolzano, KRDB Research Centre for Knowledge and Data, Bozen-Bolzano, Italy.
    Proceedings of the Sixth Image Schema Day 2022: Jönköping, Sweden, March 24-25th, 20222022Conference proceedings (editor) (Refereed)
  • 17.
    Lundgren, Emelie
    et al.
    Jönköping University, School of Education and Communication.
    Olsson, Hanna
    Jönköping University, School of Education and Communication.
    Robotars användning i förskola: En sociokulturell studie om Blue-bot och Bee-bot2023Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
    Abstract [sv]

    Denna studie handlar om robotarna Blue-bot och Bee-bot. Syftet är att ta reda på hur robotar används av utbildade pedagoger samt vilka färdigheter och kunskaper de beskriver att barnen utvecklar av att använda robotar. Frågeställningarna studien har är: “Hur beskriver de utbildade pedagogerna att de undervisar med Blue-bot eller Bee-bot?” och “Vilka färdigheter och kunskaper beskriver de utbildade pedagogerna att barnen utvecklar av att använda Blue-bot eller Bee-bot?”. Datainsamlingen gjordes via en kvalitativ metod med en fokusgrupp och tre intervjuer samt utfördes digitalt via Teams. Studien använder sig av den sociokulturella teorin för att granska resultatet. Resultatet visar att deltagarnas huvudsakliga syfte med arbetet med robotarna är att arbeta med matematik, vilket även involverar problemlösning, uppskattning av avstånd samt öva på rumsuppfattning och sekvensering. Alla delar inom matematik är färdigheter Kiliç (2022) kopplar till datalogiskt tänkande och det kan därmed sägas att det deltagarna vill att barnen ska utveckla i användandet av robotarna är datalogiskt tänkande. Resultatet visar också på att om syftet med att använda robotarna är att lära barnen programmeringsbegrepp och matematiskt tänkande är den analoga programmeringen bättre att använda än robotarna. Resultatet visar även att pedagogers frånvaro kan ge barnen större möjlighet till att utveckla det sociala samspelet samt utveckla fler färdigheter genom stöttning av andra barn.

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    Robotars användning i förskola: En sociokulturell studie om Blue-bot och Bee-bot
  • 18.
    Pomarlan, Mihai
    et al.
    Institute of Artificial Intelligence, University of Bremen, Germany.
    De Giorgis, Stefano
    University of Bologna, Bologna (BO), Italy.
    Hedblom, Maria M.
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Diab, Mohammed
    Imperial College London, London, UK.
    Tsiogkas, Nikolaos
    Department of Mechanical Engineering, KU Leuven, Heverlee (Leuven), Belgium; Core Lab ROB, Flanders Make, Heverlee, Belgium.
    Thinking in front of the box: Towards intelligent robotic action selection for navigation in complex environments using image-schematic reasoning2022In: Proceedings of the Joint Ontology Workshops 2022, Episode VIII: The Svear Sommar of Ontology / [ed] T. P. Sales, M. Hedblom & H. Tan, CEUR-WS , 2022, Vol. 3249Conference paper (Refereed)
    Abstract [en]

    One of the problems an agent faces when operating in a partially known, dynamic, sometimes unpredictable environment is to keep track of aspects of the world relevant to its task, and, if possible, restrict its attention to only these aspects. We present our first steps towards constructing a system that combines image schematic knowledge and reasoning with reactive robotics, and which enables perception that focuses on, and keeps track of, relevant entities and relationships. While our approach is more reasoning intensive than is usual in reactive robotics, the formalism we use for inference is fast and allows an agent to adjust, in real time, the complexity of its action selection procedures according to the complexity of the relevant part of the environment. We illustrate our approach with a few simulated examples of robots performing navigation tasks. In some examples, interaction with obstacles is necessary to complete the navigation tasks, adding complexity to the scenario.

  • 19.
    Saari, Ulla A.
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Business Administration. Unit of Industrial Engineering and Management, Faculty of Management & Business, Tampere University, Korkeakoulunkatu 8 P.O. Box 541, 33014, Finland.
    Tossavainen, A.
    Unit of Industrial Engineering and Management, Faculty of Management & Business, Tampere University, Korkeakoulunkatu 8 P.O. Box 541, 33014, Finland.
    Kaipainen, K.
    Unit of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Korkeakoulunkatu 1, P.O. Box 553, 33014, Finland.
    Mäkinen, S. J.
    Unit of Industrial Engineering and Management, Faculty of Management & Business, Tampere University, Korkeakoulunkatu 8 P.O. Box 541, 33014, Finland.
    Exploring factors influencing the acceptance of social robots among early adopters and mass market representatives2022In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 151, article id 104033Article in journal (Refereed)
    Abstract [en]

    When designing social robots, it is crucial to understand the diverse expectations of different kinds of innovation adopters. Different factors influence early adopters of innovations and mass market representatives’ perceptions of the usefulness of social robots. The first aim of the study was to test how applicable the technology acceptance model 3 (TAM3) is in the context of social robots. Participants’ acceptance of social robotics in a workplace environment in the fuzzy front-end (FFE) innovation phase of a robot development project was examined. Based on the findings for the model, we developed a reduced version of the TAM3 that is more applicable for social robots. The second objective was to analyze how early adopters’ and mass market representatives’ acceptance of social robots differs. Quantitative research methods were used. For early adopters, result demonstrability has a significant influence on perceived usefulness of social robots, while for mass market representatives, perceived enjoyment has a more significant influence on perceived usefulness. The findings indicate that users’ innovation adoption style influences the factors that users consider important in the usefulness of social robots. Robot developers should take these into account during the FFE innovation phase.

  • 20.
    Salim, Roaa
    et al.
    Jönköping University, School of Engineering, JTH, Industrial Engineering and Management. Jönköping University, School of Engineering, JTH. Research area Industrial Production.
    Mapulanga, Mwanza
    Jönköping University, School of Engineering, JTH. Research area Industrial Production.
    Saladi, Praveen
    Jönköping University, School of Engineering, JTH. Research area Industrial Production.
    Karltun, Anette
    Jönköping University, School of Engineering, JTH, Industrial Engineering and Management. Jönköping University, School of Engineering, JTH. Research area Industrial Production.
    Automation in the wood products industry: challenges and opportunities2016Conference paper (Refereed)
    Abstract [en]

    A stagnation of productivity increase has been observed in the Swedish wood product industry. The manufacturers believe that there is a need to invest further in automation in order to stay competitive. For this reason, the paper seeks to understand the role of automation in the Swedish wood product industry, and focuses on identifying the challenges and opportunities of automation. The following research question was addressed: What are the challenges and opportunities of automation in the wood product industry? In order to answer the research question, four case studies were conducted, each case representing a different business area. The research question was examined in terms of internal – and external challenges and opportunities. The internal challenges and opportunities examine the manufacturing, while the external challenges and opportunities examine the influence of the business environment. Findings indicate that lack of manufacturing strategies, and lack of awareness of automation technologies were some of the main challenges. Regarding the opportunities, increased profitability and competitiveness were emphasized. The identification of the challenges and opportunities of automation in the wood product industry can provide insights and be used as underlying decisions for automation investments.

  • 21.
    Svensson, Simon
    et al.
    Jönköping University, School of Engineering, JTH, Supply Chain and Operations Management.
    Wadsten, Adam
    Jönköping University, School of Engineering, JTH, Supply Chain and Operations Management.
    Kriterier för automation vid inlagring: Ett beslutsunderlag i valet av artikelplacering2019Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The role of warehouse in the value stream, plays a significant part in how well a company can satisfy the requirements of a customer. To be able to support the demand of the customers, production companies are forced to produce a bigger variety of models in the product range, which results in an increasing assortment. With an effective stock keeping, there is a potential to reduce waste and increase profitability. In the choice of warehouse-design, warehouse-operations have the opportunity of choosing between a number of different designs containing manual and automated put away.It has become common that companies are choosing a combination of automated and manual processes. With this combination of processes, the companies are faced with choices about stock locations. With the help of well developed criterias about stock locations, there is a potential to achieve a more effective put away. The purpose of the study has been formulated as ”Investigate when automation in warehouse management makes the put away process in a central warehouse more efficient”.

     

    Through a case study on a warehouse, located in Nässjö, this report will answer the research questions regarding which of the manual and automated put away processes to use. The research questions are:

    1. Which criteria affect the decision of automated or manual put away?
    2. Based on the criteria that are raised in research question 1, when is it more efficient to store carton in AS/RS in comparison with manual picking with truck in pallet racking?

    The research questions wish to lift criteria as a basis for decision making and when it is more effective to use automated put away. The warehouse that this study is based on is using both automated and manual materials handling. The specific automation system that has been the basis for this study’s result is AutoStore. This report has a qualitative approach, the methods that has been used for collecting data are interviews, observations and document studies.

     

    Dimension, process time and utilization were the three criteria that are presented in the result. Based on the pallets limit of space in the automated system, the dimension of the article is the first criteria. In addition to the article’s measurements, the articles shape and material of the package had an impact if the article would fit in the load carrier. The two put away processes include different operations that differ in how long they take to execute, which has an impact on the company's efficiency. One reason for the time consuming difference, turned out to be how the quantity affects the time for put away in AutoStore comparing with the constant time for manual put away. A time consuming operation in the process with AutoStore was the one with repacking for the systems pallet. The operation also turned out to affect the utilization rate, due to its measurement of a pallets fill rate.  The quantity of put away in combination with the dimensions of the article, affects the total utilization rate.

     

    With the result, the aim of the study is to contribute to companies that contains a combination of warehouse-design with a basis for the decision making about stock locations. 

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  • 22.
    Truong, Daniel
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Detection of motor skidding in autonomous lawn mowers: Detection of skidding in autonomous lawn mower using machine learning technique MLP with wheel motor currents and IMU.2020Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Purpose – The purpose of the thesis is to evaluate the accuracy of two different approaches of how to detect skidding in autonomous lawn mowers. Using the motor currents and inertia sensors from the mower, a neural network is applied to predict if there was a skidding or not. The usefulness of knowing when a skid happens could be of value for future developments making better autonomous decision making.

    Method – The thesis will adopt the methodology process of Design Science Research (DSR). The study begins with bringing awareness of the problem by previous knowledge and related works to skidding in wheeled robots. Thereafter an experiment is set up to generate data when the autonomous lawn mower is in conditions of skidding and non-skidding. The data collected will be processed with machine learning algorithm, multilayer perceptron.

    Findings – The findings showed high accuracies in both techniques where adding an IMU sensor in addition to motor currents showed higher accuracy then only using motor currents. Both techniques showed low number of false detections and near zero missed detections which is a preferred feature, the behavior of the autonomous lawn mower benefits more from a false detection than not detecting any at all and get stuck.

    Implications – The autonomous lawn mowers of today have a tendency of failing in the yard due to skidding in uneven or slippery terrain. The robot either reacts by assuming a collision has happened or gets no traction and gets stuck. A first step to solve this problem is by detecting such a skid to then be able to take action.

    Limitations – The results will be limited to the autonomous lawn mower of Globe Group as the data collection is made with an autonomous lawn mower of Globe Group. The mower will run on a flat outdoor grass lawn to maintain the experiment on a reasonable level for a bachelor thesis.

    Keywords – robot, autonomous, lawn mower, machine learning, detection, skidding

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  • 23.
    Valfridsson, Christopher
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Positionering av autonoma robotar utrustade med radar2020Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Purpose – The purpose of the study was to investigate what precision a radar sensor could guide an autonomous robot into a docking station.

     

    Method – The study was conducted with design science research and it was produced artifacts that were evaluated through experiments. The work was based on the hypothesis that two cube corner reflectors, mounted on a docking station, made it possible to locate a docking station with a radar sensor. The study was based on that the robot had navigated in another way to a circular area, with a radius of 0.5 meters, located 1.5 meters straight in front of the docking station. The initial experiments were designed to investigate how different locations of the reflectors affected the radar sensor's ability to detect and position the docking station from the area just mentioned. The results from these experiments was then used to design the final experiment of the study. In this a robot was equipped with a radar sensor. Two reflectors, mounted on a common stand, was used to imitate a docking station. The experiment evaluated how the location of the docking station was affected by the radar's movement and what precision a docking could be carried out with the concept.

     

    Findings – The study has shown that there are times when two cube corner reflectors mounted on a docking station can be used to guide a robot, equipped with a radar sensor, into a docking station. In the final experiment of the study, ten dockings were carried out with the concept. The largest angle deviation measured, at the robot's final position in the docking station, was three degrees and the largest lateral offset was six millimetres. The analyses of the data collected during the experiment showed that there was potential to reduce these differences.

     

    The study also showed that the ability of the radar system to locate the docking station increased when the reflectors were at different distances from the radar compared to if they were only at different angles seen from the radar.

     

    Implications – The study contributes to an increased knowledge of how a radar sensor can be used to guide an autonomous robot into a docking station. The study also provides suggestions for further research that can add additional knowledge.

     

    Limitations – A docking procedure depends on a variety of parameters such as, the environment around the docking station, which sensors are available on the robot and what the requirements are on the precision of the docking. This study has shown that a radar guided docking may be carried out for at least one combination of parameters. It has not investigated all possible limiting factors that may exist. One of these factors could be the radar's ability to distinguish a docking station in an environment with many other objects.

  • 24.
    Vilhelmsson, André
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics. 9502071419.
    Detektion av landningsbanor med maskininlärning: Optimering av maskininlärningsparametrar för videoigenkänning2019Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    PurposeThe purpose of the thesis is to use design science research to produce information to improve a support vector machine (SVM) model and to gain knowledge about optimization of hyperparameters through experiments. The objective will be answered through evaluation of performance gained by adjusting the hyperparameters. The report will also evaluate the further use of the techniques with IR cameras in combination with histogram of oriented gradients (HOG) as translated input for the SVM-model trained to detect and do a classification of runways.MethodThe method was performed through three technical experiments performed at Saab Avionics Systems. The experiments contain adjusting and optimizing settings in Saabs framework for detecting runways with the machine learning method support vector machine. The experiments with manual grid search will test and evaluate the performance of the support vector ratio, accuracy and more. Three different optimization methods will be evaluated which consists of grid search, random grid search and Bayesian optimization and all three will optimize the parameters C and gamma.FindingsThe findings indicate that all the evaluated parameters could bring positive performance impact on the SVM. Some important results were the HOG settings 6 orientation histograms, 64*64 pixels per cell, 2*2 cells per pixel, which performed best. An even dividable resolution of the image was also important for the HOG calculations. Other results indicate that the Radial Basis Function - kernel was the better choice over the linear kernel, as well as a higher amount of training data was better for a higher performance.The best performing optimization algorithm was the Bayesian optimization which found and improved three of eight testcases. The technique combining SVM with HOG-vectors for detecting runways are shown being implementable with high performance. A hardware implementation with an FPGA would be the recommended implementation which would improve classification time as well as the HOG computation time. Around 35-145 fps per detection window would be achievable with this technique with an accuracy above 99.4%.ComplicationsFew implications were found during the work apart from one. Which were the case of the detection window of one of the IR cameras (LWIR) which was a bit skewed compared to the SWIR sensor. The result contains some uncertainties of the performance compared between SWIR and LWIR sensors.LimitationsThe limitation of the report is that the training data were similar and only consisting of one runway. The accuracy performance can be a bit misleading when evaluating an imbalanced class.KeywordsSVM, HOG, runway, hyperparameters, optimization, RBF kernel, grid search, Bayesian optimization, random grid search, object detection, classification, margin for detecting window, over fitting, C, gamma, range, IR camera, training data, data augmentation, class balance in data, implementation.

  • 25.
    Winberg, Jessica
    et al.
    Jönköping University, School of Engineering, JTH, Supply Chain and Operations Management.
    Hanna, Svensson
    Jönköping University, School of Engineering, JTH, Supply Chain and Operations Management.
    En hissautomations begränsningar och tillgångar: En fallstudie som undersöker hur en hissautomation bör användas i en plockmiljö2021Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesis
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