@inproceedings{BakkZivkKroe2005,
authors="B.Bakker and Z.Zivkovic and B.Krose",
title ="Hierarchical Dynamic Programming for Robot Path Planning",
booktitle="In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems",
pages="3720-3625",
year=2005,
abstract="This paper addresses the question how robot planning
(e.g. for navigation) can be done with hierarchical maps.
We present an algorithm for hierarchical path planning for
stochastic tasks, based on Markov Decision Processes (MDPs)
and dynamic programming, that is more efficient than standard
dynamic programming for flat MDPs, because it reduces the
state space for all levels in its hierarchy and it allows reuse
of previously computed partial policies. This computational
advantage comes at the cost of some extra memory and overhead
to represent and coordinate the hierarchical system, and in some
cases somewhat longer paths to target locations. We demonstrate
the method on artificially generated MDP data, and on real
robot data from our vision-controlled robot navigating in an
ofce environment"
}