TY - GEN
T1 - Semi-Communicate Social Navigation using Deep Q Networks
AU - Park, Heung Min
AU - Jung, Donghwi
AU - Kim, Seong Woo
N1 - Funding Information:
ACKNOWLEDGMENT This work is supported by Korean Ministry of Land, Infrastructure and Transport (MOLIT) as the Innovative Talent Education Program for Smart City, and supported by the National Research Foundation of Korea (NRF) through the Ministry of Science and ICT under Grant 2021R1A2C1093957.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The research of mobile robot and pedestrian social interaction aware in human-robot interaction is focused on solving the problem of pedestrian trajectory prediction based on RNN or reinforcement learning. This method has problems such as a low success rate and an uneven path in predicting and avoiding the trajectory of a new environment and an object that is not pre-trained. Because of these problems, it is very difficult to navigate and control existing mobile robots using reinforcement learning. However, many previous reinforcement learning experiments did not consider the precise positioning design of robots and pedestrians to have a mobile robot navigation system with high success rate and safety. In order to alleviate this dilemma, this study aims to improve driving efficiency and learning safety by setting states through precise positioning design of robots and dynamic objects in Deep Q Networks.
AB - The research of mobile robot and pedestrian social interaction aware in human-robot interaction is focused on solving the problem of pedestrian trajectory prediction based on RNN or reinforcement learning. This method has problems such as a low success rate and an uneven path in predicting and avoiding the trajectory of a new environment and an object that is not pre-trained. Because of these problems, it is very difficult to navigate and control existing mobile robots using reinforcement learning. However, many previous reinforcement learning experiments did not consider the precise positioning design of robots and pedestrians to have a mobile robot navigation system with high success rate and safety. In order to alleviate this dilemma, this study aims to improve driving efficiency and learning safety by setting states through precise positioning design of robots and dynamic objects in Deep Q Networks.
KW - Mobile robot
KW - Navigation
KW - Trajectory prediction
UR - http://www.scopus.com/inward/record.url?scp=85128832273&partnerID=8YFLogxK
U2 - 10.1109/ICEIC54506.2022.9748263
DO - 10.1109/ICEIC54506.2022.9748263
M3 - Conference contribution
AN - SCOPUS:85128832273
T3 - 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022
BT - 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022
Y2 - 6 February 2022 through 9 February 2022
ER -