TY - GEN
T1 - Mobile Robot Navigation Using Learning-Based Method Based on Predictive State Representation in a Dynamic Environment
AU - Matsumoto, Kohei
AU - Kawamura, Akihiro
AU - An, Qi
AU - Kurazume, Ryo
N1 - Funding Information:
ACKNOWLEDGMENT This work was partially supported by JSPS KAKENHI Grant Number JP20H00230.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
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U2 - 10.1109/SII52469.2022.9708775
DO - 10.1109/SII52469.2022.9708775
M3 - Conference contribution
AN - SCOPUS:85126259730
T3 - 2022 IEEE/SICE International Symposium on System Integration, SII 2022
SP - 499
EP - 504
BT - 2022 IEEE/SICE International Symposium on System Integration, SII 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE/SICE International Symposium on System Integration, SII 2022
Y2 - 9 January 2022 through 12 January 2022
ER -