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  • 1.
    Kiselev, Andrey
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Sivakumar, Prasanna Kumar
    SASTRA University, Thanjavur, India.
    Swaminathan, Chittaranjan Srinivas
    SASTRA University, Thanjavur, India.
    Robot-human hand-overs in non-anthropomorphic robots2013In: 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, p. 227-228Conference paper (Refereed)
    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.
    Rudenko, Andrey
    et al.
    Örebro University, School of Science and Technology. Robotics Research, Bosch Corporate Research, Stuttgart, Germany.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Swaminathan, Chittaranjan Srinivas
    Örebro University, School of Science and Technology.
    Chadalavada, Ravi Teja
    Örebro University, School of Science and Technology.
    Arras, Kai O.
    Robotics Research, Bosch Corporate Research, Stuttgart, Germany.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    THÖR: Human-Robot Navigation Data Collection and Accurate Motion Trajectories Dataset2020In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 5, no 2, p. 676-682Article in journal (Refereed)
    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.

  • 3.
    Swaminathan, Chittaranjan Srinivas
    et al.
    Örebro University, School of Science and Technology.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Palmieri, Luigi
    Robert Bosch, GmbH Corporate Research, Germany.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Down the CLiFF: Flow-Aware Trajectory Planning under Motion Pattern Uncertainty2018In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 7403-7409Conference paper (Refereed)
    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.

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