Motion planning of a mobile robot as a discrete optimization problem

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Abstract

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.

Original languageEnglish
Pages1-6
Number of pages6
Publication statusPublished - 2001 Jan 1
Event2001 IEEE International Symposium on Assembly and Task Planning (ISATP2001) - Fukuoka, Japan
Duration: 2001 May 282001 May 29

Conference

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

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Igarashi, H. (2001). Motion planning of a mobile robot as a discrete optimization problem. 1-6. Paper presented at 2001 IEEE International Symposium on Assembly and Task Planning (ISATP2001), Fukuoka, Japan.