Path planning of a mobile robot by optimization and reinforcement learning |
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Authors: | Harukazu Igarashi |
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Affiliation: | (1) School of Engineering, Kinki University, 1 Takaya-Umenobe Higashi-Hiroshima, 739-2116 Hiroshima, Japan |
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Abstract: | At AROB5, we proposed a solution to the path planning of a mobile robot. In our approach, we formulated the problem as a discrete
optimization problem at each time step. To solve the optimization problem, we used an objective function consisting of a goal
term, a smoothness term, and a collision term. While the results of our simulation showed the effectiveness of our approach,
the values of the weights in the objective function were not given by any theoretical method. This article presents a theoretical
method using reinforcement learning for adjusting the weight parameters. We applied Williams' learning algorithm, episodic
REINFORCE, to derive a learning rule for the weight parameters. We verified the learning rule by some experiments.
This work was presented, in part, at the Sixth International Symposium on Artificial Life and Robotics, Tokyo, Japan, January
15–17, 2001 |
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Keywords: | Path planning Mobile robot Optimization problem Reinforcement learning |
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