Continuous Real Time POMCP to Find-and-Follow People by a Humanoid Service Robot

Alex Goldhoorn, Anaís Garrell, René Alquézar and Alberto Sanfeliu

IEEE-RAS International Conference on Humanoid Robots

Abstract—This study describes and evaluates two new methods for finding and following people in urban settings using a humanoid service robot: the Continuous Real-time POMCP method, and its improved extension called Adaptive Highest Belief Continuous Real-time POMCP follower. They are able to run in real-time, in large continuous environments. These methods make use of the online search algorithm Partially Observable Monte-Carlo Planning (POMCP), which in contrast to other previous approaches, can plan under uncertainty on large state spaces. We compare our new methods with a heuristic person follower and demonstrate that they obtain better results by testing them extensively in both simulated and real-life experiments. More than two hours, over 3 km, of autonomous navigation during real-life experiments have been done with a mobile humanoid robot in urban environments.


  • Heuristic Follower: The robot's goal position is set just in front of the person. When the target is not visible anymore, the robot keeps going to the last person's position. When reaching this position, the robot stays and waits for the person to appear again.

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