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  • 1.
    Kiselev, Andrey
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Loutfi, Amy
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Sivakumar, Prasanna Kumar
    SASTRA University, Thanjavur, India.
    Swaminathan, Chittaranjan Srinivas
    SASTRA University, Thanjavur, India.
    Robot-human hand-overs in non-anthropomorphic robots2013Inngår i: Proceedings of the 8th ACM/IEEE International Conference on Human-Robot Interaction, HRI'13 / [ed] Hideaki Kuzuoka, Vanessa Evers, Michita Imai, Jodi Forlizzi, IEEE Press, 2013, s. 227-228Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Robots that assist and interact with humans will inevitably require to successfully achieve the task of handing over objects. Whether it is to deliver desired objects for the elderly living in their homes or hand tools to a worker in a factory, the process of robot hand-overs is one worthy study within the human robot interaction community. While the study of object hand-overs have been studied in previous works, these works have mainly considered anthropomorphic robots, that is, robots that appear and move similar to humans. However, recent trends within robotics, and in particular domestic robotics have witnessed an increase in non-anthropomorphic robotic platforms such as moving tables, teleconferencing robots and vacuum cleaners. The study of robot hand-over for nonanthropomorphic robots and in particular the study of what constitute a successful hand-over is at focus in this paper. For the purpose of investigation, the TurtleBot, which is a moving table like device is used in a home environment.

  • 2.
    Kucner, Tomasz Piotr
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, L.
    Corporate Research, Robert Bosch GmbH, Renningen, Germany.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Closing Remarks2020Inngår i: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, s. 143-151Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    Dynamics is an inherent feature of reality. In spite of that, the domain of maps of dynamics has not received a lot of attention yet. In this book, we present solutions for building maps of dynamics and outline how to make use of them for motion planning. In this chapter, we present discuss related research question that as of yet remain to be answered, and derive possible future research directions. 

  • 3.
    Kucner, Tomasz Piotr
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, L.
    Corporate Research, Robert Bosch GmbH, Renningen, Germany .
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Introduction2020Inngår i: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, s. 1-13Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    Change and motion are inherent features of reality. The ability to recognise patterns governing changes has allowed humans to thrive in a dynamic reality. Similarly, dynamics awareness can also improve the performance of robots. Dynamics awareness is an umbrella term covering a broad spectrum of concepts. In this chapter, we present the key aspects of dynamics awareness. We introduce two motivating examples presenting the challenges for robots operating in a dynamic environment. We discuss the benefits of using spatial models of dynamics and analyse the challenges of building such models.

  • 4.
    Kucner, Tomasz Piotr
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, L.
    Corporate Research, Robert Bosch GmbH, Renningen, Germany.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Maps of Dynamics2020Inngår i: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, s. 15-32Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    The task of building maps of dynamics is the key focus of this book, as well as how to use them for motion planning. In this chapter, we present a categorisation and overview of different types of maps of dynamics. Furthermore, we give an overview of approaches to motion planning in dynamic environments, with a focus on motion planning over maps of dynamics. 

  • 5.
    Kucner, Tomasz Piotr
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, L.
    Corporate Research, Robert Bosch GmbH, Renningen, Germany.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Modelling Motion Patterns with Circular-Linear Flow Field Maps2020Inngår i: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, s. 65-113Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    The shared feature of the flow of discrete objects and continuous media is that they both can be represented as velocity vectors encapsulating direction and speed of motion. In this chapter, we present a method for modelling the flow of discrete objects and continuous media as continuous Gaussian mixture fields. The proposed model associates to each part of the environment a Gaussian mixture model describing the local motion patterns. We also present a learning method, designed to build the model from a set of sparse, noisy and incomplete observations. 

  • 6.
    Kucner, Tomasz Piotr
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, L.
    Corporate Research, Robert Bosch GmbH, Renningen, Germany.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Modelling Motion Patterns with Conditional Transition Map2020Inngår i: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, s. 33-64Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    The key idea of modelling flow of discrete objects is to capture the way they move through the environment. One method to capture the flow is to observe changes in occupancy caused by the motion of discrete objects. In this chapter, we present a method to model and learn occupancy shifts caused by an object moving through the environment. The key idea is observe temporal changes changes in the occupancy of adjacent cells, and based on the temporal offset infer the direction of the occupancy flow.

  • 7.
    Kucner, Tomasz Piotr
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, L.
    Corporate Research, Robert Bosch GmbH, Renningen, Germany.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Motion Planning Using MoDs2020Inngår i: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, s. 115-141Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    Maps of dynamics can be beneficial for motion planning. Information about motion patterns in the environment can lead to finding flow-aware paths, allowing robots to align better to the expected motion: either of other agents in the environment or the flow of air or another medium. The key idea of flow-aware motion planning is to include adherence to the flow represented in the MoD into the motion planning algorithm’s sub-units (i.e. cost function, sampling mechanism), thereby biasing the motion planner into obeying local and implicit traffic rules. 

  • 8.
    Kucner, Tomasz Piotr
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, Luigi
    Corporate Research, Robert Bosch GmbH, Renningen, Germany.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots2020Bok (Annet vitenskapelig)
    Abstract [en]

    This book describes how robots can make sense of motion in their surroundings and use the patterns they observe to blend in better in dynamic environments shared with humans.The world around us is constantly changing. Nonetheless, we can find our way and aren’t overwhelmed by all the buzz, since motion often follows discernible patterns. Just like humans, robots need to understand the patterns behind the dynamics in their surroundings to be able to efficiently operate e.g. in a busy airport. Yet robotic mapping has traditionally been based on the static world assumption, which disregards motion altogether. In this book, the authors describe how robots can instead explicitly learn patterns of dynamic change from observations, store those patterns in Maps of Dynamics (MoDs), and use MoDs to plan less intrusive, safer and more efficient paths. The authors discuss the pros and cons of recently introduced MoDs and approaches to MoD-informed motion planning, and provide an outlook on future work in this emerging, fascinating field. 

  • 9.
    Kucner, Tomasz Piotr
    et al.
    Mobile Robotics Group, School of Electrical Engineering, Aalto University, Finland; Finnish Center for Artificial Intelligence, Finland.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Mghames, Sariah
    L-CAS, School of Computer Science, University of Lincoln, Lincoln, UK.
    Palmieri, Luigi
    BOSCH Corporate Research, Renningen, Germany.
    Verdoja, Francesco
    Intelligent Robotics Group, School of Electrical Engineering, Aalto University, Finland.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Krajnik, Tomas
    Artificial Intelligence Center, Czech Technical University, Praha, Czechia.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Bellotto, Nicola
    L-CAS, School of Computer Science, University of Lincoln, Lincoln, UK; Department of Information Engineering, Univeristy of Padua, Padova, Italy.
    Hanheide, Marc
    L-CAS, School of Computer Science, University of Lincoln, Lincoln, UK.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik. Technical Univeristy of Munich, Munich, Germany.
    Survey of maps of dynamics for mobile robots2023Inngår i: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 42, nr 11, s. 977-1006Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area.

  • 10.
    Kucner, Tomasz Piotr
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, L.
    Corporate Research Robert Bosch GmbH, Renningen, Germany.
    Preface2020Inngår i: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, s. vii-xKapittel i bok, del av antologi (Fagfellevurdert)
  • 11.
    Molina, Sergi
    et al.
    University of Lincoln, Lincoln, U.K.
    Mannucci, Anna
    Robert Bosch GmbH, Renningen, Germany.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Adolfsson, Daniel
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Andreasson, Henrik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Hamad, Mazin
    Technical University of Munich, Munich, Germany.
    Abdolshah, Saeed
    Technical University of Munich, Munich, Germany.
    Chadalavada, Ravi Teja
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, Luigi
    Robert Bosch GmbH, Renningen, Germany.
    Linder, Timm
    Robert Bosch GmbH, Renningen, Germany.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Kucner, Tomasz Piotr
    Aalto University, Aalto, Finland.
    Hanheide, Marc
    University of Lincoln, Lincoln, U.K..
    Fernandez-Carmona, Manuel
    University of Lincoln, Lincoln, U.K..
    Cielniak, Grzegorz
    University of Lincoln, Lincoln, U.K..
    Duckett, Tom
    University of Lincoln, Lincoln, U.K..
    Pecora, Federico
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Bokesand, Simon
    Kollmorgen Automation AB, Mölndal, Sweden.
    Arras, Kai O.
    Robert Bosch GmbH, Renningen, Germany.
    Haddadin, Sami
    Technical University of Munich, Munich, Germany.
    Lilienthal, Achim J
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    The ILIAD Safety Stack: Human-Aware Infrastructure-Free Navigation of Industrial Mobile Robots2023Inngår i: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223XArtikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Current intralogistics services require keeping up with e-commerce demands, reducing delivery times and waste, and increasing overall flexibility. As a consequence, the use of automated guided vehicles (AGVs) and, more recently, autonomous mobile robots (AMRs) for logistics operations is steadily increasing.

  • 12.
    Rudenko, Andrey
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik. Robotics Research, Bosch Corporate Research, Stuttgart, Germany.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Chadalavada, Ravi Teja
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Arras, Kai O.
    Robotics Research, Bosch Corporate Research, Stuttgart, Germany.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    THÖR: Human-Robot Navigation Data Collection and Accurate Motion Trajectories Dataset2020Inngår i: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 5, nr 2, s. 676-682Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Understanding human behavior is key for robots and intelligent systems that share a space with people. Accordingly, research that enables such systems to perceive, track, learn and predict human behavior as well as to plan and interact with humans has received increasing attention over the last years. The availability of large human motion datasets that contain relevant levels of difficulty is fundamental to this research. Existing datasets are often limited in terms of information content, annotation quality or variability of human behavior. In this paper, we present THÖR, a new dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for position, head orientation, gaze direction, social grouping, obstacles map and goal coordinates. THÖR also contains sensor data collected by a 3D lidar and involves a mobile robot navigating the space. We propose a set of metrics to quantitatively analyze motion trajectory datasets such as the average tracking duration, ground truth noise, curvature and speed variation of the trajectories. In comparison to prior art, our dataset has a larger variety in human motion behavior, is less noisy, and contains annotations at higher frequencies.

  • 13.
    Rudenko, Andrey
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Chadalavada, Ravi Teja
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Arras, Kai Oliver
    Bosch Corporate Research, Renningen, Germany.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Benchmarking Human Motion Prediction Methods2020Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    In this extended abstract we present a novel dataset for benchmarking motion prediction algorithms. We describe our approach to data collection which generates diverse and accurate human motion in a controlled weakly-scripted setup. We also give insights for building a universal benchmark for motion prediction.

    Fulltekst (pdf)
    Benchmarking Human Motion Prediction Methods
  • 14.
    Swaminathan, Chittaranjan Srinivas
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Quantitative Metrics for Execution-Based Evaluation of Human-Aware Global Motion Planning2020Inngår i: HRI 2020 Workshop on Test Methods and Metrics for Effective HRI in Real World Human-Robot Teams, 2020Konferansepaper (Annet vitenskapelig)
  • 15.
    Swaminathan, Chittaranjan Srinivas
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, Luigi
    Robert Bosch, GmbH Corporate Research, Germany.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Down the CLiFF: Flow-Aware Trajectory Planning under Motion Pattern Uncertainty2018Inngår i: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2018, s. 7403-7409Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this paper we address the problem of flow-aware trajectory planning in dynamic environments considering flow model uncertainty. Flow-aware planning aims to plan trajectories that adhere to existing flow motion patterns in the environment, with the goal to make robots more efficient, less intrusive and safer. We use a statistical model called CLiFF-map that can map flow patterns for both continuous media and discrete objects. We propose novel cost and biasing functions for an RRT* planning algorithm, which exploits all the information available in the CLiFF-map model, including uncertainties due to flow variability or partial observability. Qualitatively, a benefit of our approach is that it can also be tuned to yield trajectories with different qualities such as exploratory or cautious, depending on application requirements. Quantitatively, we demonstrate that our approach produces more flow-compliant trajectories, compared to two baselines.

    Fulltekst (pdf)
    Down The CLiFF: Flow-aware Trajectory Planning under Motion Pattern Uncertainty
  • 16.
    Swaminathan, Chittaranjan Srinivas
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Kucner, Tomasz Piotr
    Finnish Centre for Artificial Intelligence (FCAI), Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, Luigi
    Robert Bosch GmbH Corporate Research, Stuttgart, Germany.
    Molina, Sergi
    Lincoln Centre for Autonomous Systems, School of Computer Science, University of Lincoln, Lincoln, United Kingdom.
    Mannucci, Anna
    School of Science and Technology, Örebro University, Örebro, Sweden.
    Pecora, Federico
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Benchmarking the utility of maps of dynamics for human-aware motion planning2022Inngår i: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 9, artikkel-id 916153Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Robots operating with humans in highly dynamic environments need not only react to moving persons and objects but also to anticipate and adhere to patterns of motion of dynamic agents in their environment. Currently, robotic systems use information about dynamics locally, through tracking and predicting motion within their direct perceptual range. This limits robots to reactive response to observed motion and to short-term predictions in their immediate vicinity. In this paper, we explore how maps of dynamics (MoDs) that provide information about motion patterns outside of the direct perceptual range of the robot can be used in motion planning to improve the behaviour of a robot in a dynamic environment. We formulate cost functions for four MoD representations to be used in any optimizing motion planning framework. Further, to evaluate the performance gain through using MoDs in motion planning, we design objective metrics, and we introduce a simulation framework for rapid benchmarking. We find that planners that utilize MoDs waste less time waiting for pedestrians, compared to planners that use geometric information alone. In particular, planners utilizing both intensity (proportion of observations at a grid cell where a dynamic entity was detected) and direction information have better task execution efficiency.

    Fulltekst (pdf)
    Benchmarking the utility of maps of dynamics for human-aware motion planning
  • 17.
    Vintr, Tomas
    et al.
    Artificial Intelligence Center, Czech Technical University.
    Molina, Sergi
    Lincoln Centre for Autonomous Systems (L-CAS), University of Lincoln.
    Senanayake, Ransalu
    Stanford University.
    Broughton, George
    Artificial Intelligence Center, Czech Technical University.
    Yan, Zhi
    Distributed Artificial Intelligence and Knowledge Laboratory (CIAD), University of Technology of Belfort-Montbeliard (UTBM), France.
    Ulrich, Jiri
    Artificial Intelligence Center, Czech Technical University.
    Kucner, Tomasz P.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Majer, Filip
    Artificial Intelligence Center, Czech Technical University.
    Stachova, Maria
    University of Matej Bel, Banska Bystrica, Slovakia.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Krajnik, Tomas
    Artificial Intelligence Center, Czech Technical University.
    Time-varying Pedestrian Flow Models for Service Robots2019Inngår i: 2019 European Conference on Mobile Robots (ECMR), IEEE, 2019, artikkel-id 8870909Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We present a human-centric spatio-temporal model for service robots operating in densely populated environments for long time periods. The method integrates observations of pedestrians performed by a mobile robot at different locations and times into a memory efficient model, that represents the spatial layout of natural pedestrian flows and how they change over time. To represent temporal variations of the observed flows, our method does not model the time in a linear fashion, but by several dimensions wrapped into themselves. This representation of time can capture long-term (i.e. days to weeks) periodic patterns of peoples’ routines and habits. Knowledge of these patterns allows making long-term predictions of future human presence and walking directions, which can support mobile robot navigation in human-populated environments. Using datasets gathered by a robot for several weeks, we compare the model to state-of-the-art methods for pedestrian flow modelling.

    Fulltekst (pdf)
    Time-varying Pedestrian Flow Models for Service Robots
  • 18.
    Vintr, Tomas
    et al.
    Czech Technical University in Prague, Prague, the Czech Republic.
    Yan, Zhi
    University of Technology of Belfort-Montbeliard (UTBM), France.
    Eyisoy, Kerem
    Department of Computer Engineering, Faculty of Engineering, Marmara University, Turkey.
    Kubis, Filip
    Czech Technical University in Prague, Prague, the Czech Republic.
    Blaha, Jan
    Czech Technical University in Prague, Prague, the Czech Republic.
    Ulrich, Jiri
    Czech Technical University in Prague, Prague, the Czech Republic.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Molina, Sergi
    University of Lincoln, UK.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Cielniak, Grzegorz
    University of Lincoln, UK.
    Faigl, Jan
    Czech Technical University in Prague, Prague, the Czech Republic.
    Duckett, Tom
    University of Lincoln, UK.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Krajnik, Tomas
    Czech Technical University in Prague, Prague, the Czech Republic.
    Natural Criteria for Comparison of Pedestrian Flow Forecasting Models2020Inngår i: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE Press, 2020, s. 11197-11204Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Models of human behaviour, such as pedestrian flows, are beneficial for safe and efficient operation of mobile robots. We present a new methodology for benchmarking of pedestrian flow models based on the afforded safety of robot navigation in human-populated environments. While previous evaluations of pedestrian flow models focused on their predictive capabilities, we assess their ability to support safe path planning and scheduling. Using real-world datasets gathered continuously over several weeks, we benchmark state-of-theart pedestrian flow models, including both time-averaged and time-sensitive models. In the evaluation, we use the learned models to plan robot trajectories and then observe the number of times when the robot gets too close to humans, using a predefined social distance threshold. The experiments show that while traditional evaluation criteria based on model fidelity differ only marginally, the introduced criteria vary significantly depending on the model used, providing a natural interpretation of the expected safety of the system. For the time-averaged flow models, the number of encounters increases linearly with the percentage operating time of the robot, as might be reasonably expected. By contrast, for the time-sensitive models, the number of encounters grows sublinearly with the percentage operating time, by planning to avoid congested areas and times.

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