Alle Publikationen
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2019
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(2019): Human Understanding of Robot Motion: The Role of Velocity and Orientation. In: International Journal of Social Robotics 11 (1), S. 75-88. DOI: 10.1007/s12369-018-0493-4
DOI: https://doi.org/10.1007/s12369-018-0493-4 Abstract: A general problem in human–robot interaction is how to test the quality of single robot behavior, in order to develop robust and human-acceptable skills. The most typical approach are user tests with subjective measures (questionnaires). We propose a new experimental paradigm that combines subjective measures with an objective behavioral measure, namely viewing times of images viewed as self-paced slide show. We applied this paradigm to human-aware robot navigation. With three experiments, we studied the influence of two aspects of robot motion: velocity profiles and the robot’s orientation. A decreasing velocity profile influenced the predictability of the observed motion, and robot orientations diverting from the robot’s motion vector caused reduced perceived autonomy ratings. We conclude that the viewing time paradigm is a promising tool for studying human-aware robot behavior and that the design of human-aware robot navigation needs to consider both the velocity and the orientation of robots.
2017
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(2017): Toward Socially Aware Robot Navigation in Dynamic and Crowded Environments. A Proactive Social Motion Model. In: IEEE Transactions on Automation Science and Engineering 14 (4), S. 1743-1760. DOI: 10.1109/TASE.2017.2731371
Abstract: Safe and social navigation is the key to deploying a mobile service robot in a human-centered environment. Widespread acceptability of mobile service robots in daily life is hindered by robot's inability to navigate in crowded and dynamic human environments in a socially acceptable way that would guarantee human safety and comfort. In this paper, we propose an effective proactive social motion model (PSMM) that enables a mobile service robot to navigate safely and socially in crowded and dynamic environments. The proposed method considers not only human states (position, orientation, motion, field of view, and hand poses) relative to the robot but also social interactive information about human-object and human group interactions. This allows development of the PSMM that consists of elements of an extended social force model and a hybrid reciprocal velocity obstacle technique. The PSMM is then combined with a path planning technique to generate a motion planning system that drives a mobile robot in a socially acceptable manner and produces respectful and polite behaviors akin to human movements. Note to Practitioners-In this paper, we validated the effectiveness and feasibility of the proposed proactive social motion model (PSMM) through both simulation and real-world experiments under the newly proposed human comfortable safety indices. To do that, we first implemented the entire navigation system using the open-source robot operating system. We then installed it in a simulated robot model and conducted experiments in a simulated shopping mall-like environment to verify its effectiveness. We also installed the proposed algorithm on our mobile robot platform and conducted experiments in our office-like laboratory environment. Our results show that the developed socially aware navigation framework allows a mobile robot to navigate safely, socially, and proactively while guaranteeing human safety and comfort in crowded and dynamic environments. In this paper, we examined the proposed PSMM with a set of predefined parameters selected based on our empirical experiences about the robot mechanism and selected social environment. However, in fact a mobile robot might need to adapt to various contextual and cultural situations in different social environments. Thus, it should be equipped with an online adaptive interactive learning mechanism allowing the robot to learn to auto-adjust their parameters according to such embedded environments. Using machine learning techniques, e.g., inverse reinforcement learning [1] to optimize the parameter set for the PSMM could be a promising research direction to improve adaptability of mobile service robots in different social environments. In the future, we will evaluate the proposed framework based on a wider variety of scenarios, particularly those with different social interaction situations and dynamic environments. Furthermore, various kinds of social cues and signals introduced in [2] and [3] will be applied to extend the proposed framework in more complicated social situations and contexts. Last but not least, we will investigate different machine learning techniques and incorporate them in the PSMM in order to allow the robot to automatically adapt to diverse social environments.
Keywords: Disziplin, Favoriten, Human comfortable safety, Ingenieurswissenschaft, Mensch-Technik-Relationen (MTR), mobile service robots, Modelle/Theorien, proactive social motion model (PSMM), Realtechnik, Robotics, Serviceroboter, social field and polite, social robots, socially aware robot navigation, Soziale Robotik, Technik 2015
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(2015): From Proxemics Theory to Socially-Aware Navigation: A Survey. In: International Journal of Social Robotics 7 (2), S. 137-153. DOI: 10.1007/s12369-014-0251-1
DOI: https://doi.org/10.1007/s12369-014-0251-1 Abstract: In the context of a growing interest in modelling human behavior to increase the robots’ social abilities, this article presents a survey related to socially-aware robot navigation. It presents a review from sociological concepts to social robotics and human-aware navigation. Social cues, signals and proxemics are discussed. Socially aware behavior in terms of navigation is tackled also. Finally, recent robotic experiments focusing on the way social conventions and robotics must be linked is presented.
2013
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(2013) : Feature-Based Prediction of Trajectories for Socially Compliant Navigation In: Roy, Nicholas; Newman, Paul; Srinivasa, Siddhartha (Hg.): Robotics: Science and systems VIII: Cambridge, Massachusetts: The MIT Press
DOI: https://doi.org/10.7551/mitpress/9816.003.0030 Abstract: Mobile robots that operate in a shared environmentwith humans need the ability to predict the movements ofpeople to better plan their navigation actions. In this paper, wepresent a novel approach to predict the movements of pedestrians.Our method reasons about entire trajectories that arise frominteractions between people in navigation tasks. It applies amaximum entropy learning method based on features that capturerelevant aspects of the trajectories to determine the probabilitydistribution that underlies human navigation behavior. Hence, ourapproach can be used by mobile robots to predict forthcominginteractions with pedestrians and thus react in a socially compliantway. In extensive experiments, we evaluate the capability andaccuracy of our approach and demonstrate that our algorithmoutperforms the popular social forces method, a state-of-the-artapproach. Furthermore, we show how our algorithm can be usedfor autonomous robot navigation using a real robot.
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