Mobile Robot Navigation Using Learning-Based Method Based on Predictive State Representation in a Dynamic Environment

Kohei Matsumoto, Akihiro Kawamura, Qi An, Ryo Kurazume

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

2 被引用数 (Scopus)

抄録

Mobile robot navigation in a dynamic environment with pedestrians is essential for service robots operating in a living environment. Accordingly, the robot needs to understand and predict the behavior of pedestrians. However, predicting pedestrian behavior in advance is difficult because human behavior may be affected by factors that cannot be directly observed or modeled in advance, such as intentions and environmental influences. In addition, pedestrian behavior may be affected by the behavior of the robot.In this study, we apply a deep reinforcement learning method based on a novel predictive state representation (PSR) model to mobile robot navigation for realizing a navigation method considering the changes in pedestrian behavior caused by robot actions and other pedestrians. In addition, we propose two methods for integrating the states of the PSRs corresponding to each pedestrian and evaluate these methods in situations where the number of pedestrians differs between learning and testing.

本文言語英語
ホスト出版物のタイトル2022 IEEE/SICE International Symposium on System Integration, SII 2022
出版社Institute of Electrical and Electronics Engineers Inc.
ページ499-504
ページ数6
ISBN(電子版)9781665445405
DOI
出版ステータス出版済み - 2022
イベント2022 IEEE/SICE International Symposium on System Integration, SII 2022 - Virtual, Narvik, ノルウェー
継続期間: 1月 9 20221月 12 2022

出版物シリーズ

名前2022 IEEE/SICE International Symposium on System Integration, SII 2022

会議

会議2022 IEEE/SICE International Symposium on System Integration, SII 2022
国/地域ノルウェー
CityVirtual, Narvik
Period1/9/221/12/22

!!!All Science Journal Classification (ASJC) codes

  • 人工知能
  • ハードウェアとアーキテクチャ
  • 生体医工学
  • 制御およびシステム工学
  • 機械工学
  • 制御と最適化

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