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Motion planning algorithm for
nonholonomic autonomous underwater vehicle in disturbance
using reinforcement learning and teaching
method
Kawano,
H. Ura, T.
Inst. of Ind. Sci., Univ. of
Tokyo;
This paper
appears in: Robotics and Automation, 2002. Proceedings.
ICRA '02. IEEE International Conference on
On page(s): 4032- 4038
vol.4
2002
ISSN:
Number of Pages: 4 vol.lxxiv+4353
INSPEC Accession Number:
7387886
Abstract:
A training algorithm for motion planning
of a nonholonomic autonomous underwater vehicle (AUV) in the
strong water current is proposed in this paper. The proposed
algorithm can be applied in the environment with obstacles
placed in arbitrary configuration. In order to realize these
functions, the Q-learning and teaching method are introduced
and a multilayer structure is proposed. By introducing
Q-learning, the motion of the nonholonomic AUV can be suitably
treated. Taking advantage of the Baysian net, a motion
planning algorithm in the case of an existence of obstacles,
is derived automatically from the learned knowledge. The
multilayer structure accelerates the learning process. Results
of the demonstration by the simulation of control of R-One
robot show the high performance of proposed algorithm.
Index Terms:
belief
networks learning (artificial
intelligence) mobile
robots path
planning state-space
methods underwater
vehicles
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