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

Kohei Matsumoto, Akihiro Kawamura, Qi An, Ryo Kurazume

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2022 IEEE/SICE International Symposium on System Integration, SII 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages499-504
Number of pages6
ISBN (Electronic)9781665445405
DOIs
Publication statusPublished - 2022
Event2022 IEEE/SICE International Symposium on System Integration, SII 2022 - Virtual, Narvik, Norway
Duration: Jan 9 2022Jan 12 2022

Publication series

Name2022 IEEE/SICE International Symposium on System Integration, SII 2022

Conference

Conference2022 IEEE/SICE International Symposium on System Integration, SII 2022
Country/TerritoryNorway
CityVirtual, Narvik
Period1/9/221/12/22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Hardware and Architecture
  • Biomedical Engineering
  • Control and Systems Engineering
  • Mechanical Engineering
  • Control and Optimization

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