Improved Markov Models for Indoor Surveillance

In this paper we look at the problem of searching a human intruder in a closed environment with a small group of mobile robots. In this context motion models for the intruder play an important role for planning the coordination of the robots. Often, simple Brownian motion models are used for this purpose. However, the assumed completely random change of direction in each time step is very unrealistic. We present an improved Markovian motion model that takes the intended motion direction of a person into account in order to achieve a more realistic motion prediction. This model is then used to estimate a probability distribution of an intruder´s location within the environment. We develop a greedy algorithm that employs this distribution to coordinate the search of the environment by a group of robots. Finally, we compare our algorithm to two simple search methods and evaluate its behavior in simulation experiments