Fuzzy modeling, control and prediction in human-robot systems

Rainer Palm, Ravi Chadalavada, Achim J. Lilienthal

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations

Abstract

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 in which 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.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Pages149-177
Number of pages29
DOIs
StatePublished - 2019
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
Volume792
ISSN (Print)1860-949X

Keywords

  • Fuzzy control
  • Fuzzy modeling
  • Human intentions
  • Human-robot interaction
  • Obstacle avoidance
  • Prediction
  • Velocity obstacles

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