  We consider the problem of people search by a mobile social robot in case of
a situation that cannot be solved by the robot alone. Examples are physically
opening a closed door or operating an elevator. Based on the Behavior Tree
framework, we create a modular and easily extendable action sequence with the
goal of finding a person to assist the robot. By decomposing the Behavior Tree
as a Discrete Time Markov Chain, we obtain an estimate of the probability and
rate of success of the options for action, especially where the robot should
wait or search for people.In a real-world experiment, the presented method is
compared with other common approaches in a total of 588 test runs over the
course of one week, starting at two different locations in a university
building. We show our method to be superior to other approaches in terms of
success rate and duration until a finding person and returning to the start
location.
