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Adapting Navigation Strategies Using Learned Motion Patterns of People

We propose a method for adapting the behavior of a mobile robot according to the activities of the people in its surrounding. Our approach uses learned motion patterns of persons. Whenever the robot detects a person it computes a probabilistic estimate about which motion pattern the person might be engaged in. During path planning it then uses this belief to improve its navigation behavior. In different practical experiments carried out on a real robot we demonstrate that our approach allows a robot to quickly adapt its navigation plans according to the activities of the persons in its surrounding.

Related publication:

  • Our mobile robot Albert moves into a doorway to let a person pass by (mpg-video).
  • Albert moves forward and waits until the likelihood of interfering with the person is low enough (mpg-video).
  • Albert moves away from a doorway to let a person enter the corresponding room (mpg-video).
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