Motion planning of a mobile robot as a discrete optimization problem

H. Igarashi

研究成果: Paper査読

1 被引用数 (Scopus)

抄録

In a previous paper, we proposed a solution to motion 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. In this paper, we propose a theoretical method using reinforcement learning for adjusting weight parameters in the objective functions. However, the conventional Q-learning method cannot be applied to a non-Markov decision process, which is caused by the smoothness term. Thus, we applied William's learning algorithm, episodic REINFORCE, to derive a learning rule for the weight parameters. This maximizes a value function stochastically. We verified the learning rule by some experiments.

本文言語English
ページ1-6
ページ数6
出版ステータスPublished - 2001 1月 1
外部発表はい
イベント2001 IEEE International Symposium on Assembly and Task Planning (ISATP2001) - Fukuoka, Japan
継続期間: 2001 5月 282001 5月 29

Conference

Conference2001 IEEE International Symposium on Assembly and Task Planning (ISATP2001)
国/地域Japan
CityFukuoka
Period01/5/2801/5/29

ASJC Scopus subject areas

  • 工学(全般)

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