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.
2011
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(2011) : Please do not disturb! Minimum interference coverage for social robots In: Staff, IEEE: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011): San Francisco, CA: 9/25/2011 - 9/30/2011. IEEE Staff: [Place of publication not identified]: IEEE, S. 1968-1973
Abstract: In this paper we address the problem of human-aware coverage planning. We first present an approach to learn and model human activity events in a probabilistic spatio-temporal map using spatial Poisson processes. We then propose a coverage planner for paths that minimize the interference probability with people. To this end, we pose the coverage problem as an asymmetric traveling salesman problem with time-dependent costs (ATDTSP) derived from the information in the map. The approach enables a noisy robotic vacuum in a home scenario, for instance, to learn to avoid busy places at certain times of the day such as the kitchen at lunch time. We evaluate the planner using a simulator of people in a home environment to generate typical weekday activity patterns. In the experiments with a regular TSP planner and two modified TSP heuristics, the proposed coverage planner significantly reduces interference with people in terms of number of disturbed persons and overall disturbance time.
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