People Find-and-Follow Behavior for Service Robots using Continuous Belief Maps

Alex Goldhoorn, Anaís Garrell, Fernando Herrero, René Alquézar and Alberto Sanfeliu

Abstract— Find-and-follow is an important behavior for social robots which assist people. We introduce an on-line planning method for the robot: Continuous Real-time POMCP (CR-POMCP). It uses Partially Observable Monte-Carlo Planning (POMCP), which, in contrast to most other planning algorithms, can plan under uncertainty, on large state spaces, and real-time. Our method, CR-POMCP, also works in continuous state spaces, and moreover takes into account sensory noise, false negatives, and false positives. Comparisons have been done with a Heuristic Follower, which simply follows the person. Two extensions to CR-POMCP were tested to improve the robot’s performance when doing real-life experiments. All variants tested in simulation were found to be working better than the Heuristic Follower. Dynamic obstacle were introduced in simulation by adding randomly walking people. Real-life experiments have been done during a week with a mobile service robot in three urban environments of Barcelona with other people walking around.

On this page videos are shown which give an overview of the experiments done with the find-an-follow methods. Like explained in the article, the robot has to search and follow the person. The person to follow is recognized using AR Markers. An explanation of the the different areas of the video is given below. More details about the used urban environments are given on this page, where they also can be downloaded.



In the video the experiment is shown in three areas: