  We address the problem of controlling a mobile robot to explore a partially
known environment. The robot's objective is the maximization of the amount of
information collected about the environment. We formulate the problem as a
partially observable Markov decision process (POMDP) with an
information-theoretic objective function, and solve it applying forward
simulation algorithms with an open-loop approximation. We present a new
sample-based approximation for mutual information useful in mobile robotics.
The approximation can be seamlessly integrated with forward simulation planning
algorithms. We investigate the usefulness of POMDP based planning for
exploration, and to alleviate some of its weaknesses propose a combination with
frontier based exploration. Experimental results in simulated and real
environments show that, depending on the environment, applying POMDP based
planning for exploration can improve performance over frontier exploration.
