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Fuzzy Modeling, Control and Prediction in Human-Robot Systems
Örebro University, School of Science and Technology. (AASS MRO Lab)
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0002-8380-4113
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0003-0217-9326
2019 (English)In: Computational Intelligence: International Joint Conference, IJCCI2016 Porto, Portugal, November 9–11,2016 Revised Selected Papers / [ed] Juan Julian Merelo, Fernando Melício José M. Cadenas, António Dourado, Kurosh Madani, António Ruano, Joaquim Filipe, Switzerland: Springer Publishing Company, 2019, p. 149-177Chapter in book (Refereed)
Abstract [en]

A safe and synchronized interaction between human agents and robots in shared areas requires both long distance prediction of their motions and an appropriate control policy for short distance reaction. In this connection recognition of mutual intentions in the prediction phase is crucial to improve the performance of short distance control.We suggest an approach for short distance control inwhich the expected human movements relative to the robot are being summarized in a so-called “compass dial” from which fuzzy control rules for the robot’s reactions are derived. To predict possible collisions between robot and human at the earliest possible time, the travel times to predicted human-robot intersections are calculated and fed into a hybrid controller for collision avoidance. By applying the method of velocity obstacles, the relation between a change in robot’s motion direction and its velocity during an interaction is optimized and a combination with fuzzy expert rules is used for a safe obstacle avoidance. For a prediction of human intentions to move to certain goals pedestrian tracks are modeled by fuzzy clustering, and trajectories of human and robot agents are extrapolated to avoid collisions at intersections. Examples with both simulated and real data show the applicability of the presented methods and the high performance of the results.

Place, publisher, year, edition, pages
Switzerland: Springer Publishing Company, 2019. p. 149-177
Series
Studies in Computational Intelligence, ISSN 1860-949X, E-ISSN 1860-9503 ; 792
Keywords [en]
Fuzzy control, Fuzzy modeling, Prediction, Human-robot interaction, Human intentions, Obstacle avoidance, Velocity obstacles
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-79743DOI: 10.1007/978-3-319-99283-9ISBN: 978-3-319-99282-2 (print)ISBN: 978-3-319-99283-9 (electronic)OAI: oai:DiVA.org:oru-79743DiVA, id: diva2:1391193
Funder
Knowledge Foundation, 20140220Available from: 2020-02-03 Created: 2020-02-03 Last updated: 2020-02-05Bibliographically approved

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Palm, RainerChadalavada, Ravi TejaLilienthal, Achim

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