Replanning: A powerful planning strategy for hard kinodynamic problems

Publication Type:

Conference Paper

Authors:

,

Source:

IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France, p.1667-1672 (2008)

ISBN:

978-1-4244-2058-2

URL:

http://www.kavrakilab.org/sites/default/files/iros08replanning.pdf

Abstract:

A series of kinodynamic sampling-based planners
have appeared over the last decade to deal with high dimen-
sional problems for robots with realistic motion constraints. Yet,
offline sampling-based planners only work in static and known
environments, suffer from unbounded memory requirements
and the produced paths tend to contain a lot of unnecessary
maneuvers. This paper describes an online replanning algo-
rithm which is flexible and extensible. Our results show that
using a sampling-based planner in a loop, we can guide the
robot to its goal using a low dimensional navigation function.
We obtain higher success rates and shorter solution paths in a
series of problems using only bounded memory.