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Foreword
This thesis is part of a M. Sc. project in computer science at the school of Electrical
Engineering, Royal Institute of Technology. The M. Sc. project was carried out at
the Center for Autonomous Systems (CAS) during 2002-2003. CAS is a research
center that is a part of NADA at the Royal Institute of Technology (KTH) in Stockholm, Sweden. The center does research in (semi-) autonomous systems including
mobile robot systems for manufacturing and domestic applications.
The original goal of this project was to evaluate real-time Linux derivatives
for use in robotic control and investigate the possibility of using the reinforcement
learning (RL) paradigm to perform path planning. While doing research on path
planning I was intrigued by the apparent success of the Lazy PRM planner described
in the excellent work done by Robert Bohlin [1], which I can recommend anyone
interested in the subject to read. Also I realized that PRM planners, such as that
described by Bohlin, would experience difficulties if the configuration space contained
narrow passages. Feeling that helping to solve the narrow passage problem of PRM
planners would be a better contribution to the field of robotics than a new way of
doing path planning, I modified the topic of the project to include a study of the
narrow passage problem with PRM planners. Because of the time limits associated
with this project I had to abandon the investigation of using the RL paradigm to
do path planning.
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