The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
입자 필터는 비선형 및 비가우시안 시스템의 상태 추정 문제에 널리 사용되었습니다. 성능은 각 대상 시스템에 대해 사용자가 설계해야 하는 특정 시스템 및 측정 모델에 따라 달라집니다. 본 논문에서는 입자 필터에 대한 이러한 모델을 설계하는 새로운 방법을 제안합니다. 이는 입자 필터의 두 모델에 포함된 임의성을 강화학습 정책에 할당하여 입자 필터 설계 프로세스를 강화학습의 프레임워크로 해석하는 수치 최적화 방법입니다. 이 방법에서는 입자 필터에 의한 추정을 반복적으로 수행하고, 두 모델을 결정하는 매개변수는 추정 결과에 따라 점진적으로 업데이트됩니다. 입자 필터의 추정 정확도, 입자의 분산, 매개변수의 우도, 매개변수의 정규화 항 등 다양한 목적 함수를 최적화할 수 있다는 장점이 있습니다. 최적화 계산이 확률 1에 수렴하도록 보장하는 조건을 도출한다. 또한, 제안한 방법이 실제 규모의 문제에 적용될 수 있음을 보여주기 위해 필수 기술인 이동 로봇 위치 파악을 위한 입자 필터를 설계한다. 자율 항법. 수치 시뮬레이션을 통해 제안된 방법이 기존 방법에 비해 위치 파악 정확도가 더욱 향상됨을 입증하였다.
Ryota YOSHIMURA
Kyoto University,Tokyo Metropolitan Industrial Technology Research Institute
Ichiro MARUTA
Kyoto University
Kenji FUJIMOTO
Kyoto University
Ken SATO
Tokyo Metropolitan Industrial Technology Research Institute
Yusuke KOBAYASHI
Tokyo Metropolitan Industrial Technology Research Institute
입자 필터, 상태 추정, 강화 학습, 수렴, 모바일 로봇 국산화
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Ryota YOSHIMURA, Ichiro MARUTA, Kenji FUJIMOTO, Ken SATO, Yusuke KOBAYASHI, "Particle Filter Design Based on Reinforcement Learning and Its Application to Mobile Robot Localization" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 5, pp. 1010-1023, May 2022, doi: 10.1587/transinf.2021EDP7192.
Abstract: Particle filters have been widely used for state estimation problems in nonlinear and non-Gaussian systems. Their performance depends on the given system and measurement models, which need to be designed by the user for each target system. This paper proposes a novel method to design these models for a particle filter. This is a numerical optimization method, where the particle filter design process is interpreted into the framework of reinforcement learning by assigning the randomnesses included in both models of the particle filter to the policy of reinforcement learning. In this method, estimation by the particle filter is repeatedly performed and the parameters that determine both models are gradually updated according to the estimation results. The advantage is that it can optimize various objective functions, such as the estimation accuracy of the particle filter, the variance of the particles, the likelihood of the parameters, and the regularization term of the parameters. We derive the conditions to guarantee that the optimization calculation converges with probability 1. Furthermore, in order to show that the proposed method can be applied to practical-scale problems, we design the particle filter for mobile robot localization, which is an essential technology for autonomous navigation. By numerical simulations, it is demonstrated that the proposed method further improves the localization accuracy compared to the conventional method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7192/_p
부
@ARTICLE{e105-d_5_1010,
author={Ryota YOSHIMURA, Ichiro MARUTA, Kenji FUJIMOTO, Ken SATO, Yusuke KOBAYASHI, },
journal={IEICE TRANSACTIONS on Information},
title={Particle Filter Design Based on Reinforcement Learning and Its Application to Mobile Robot Localization},
year={2022},
volume={E105-D},
number={5},
pages={1010-1023},
abstract={Particle filters have been widely used for state estimation problems in nonlinear and non-Gaussian systems. Their performance depends on the given system and measurement models, which need to be designed by the user for each target system. This paper proposes a novel method to design these models for a particle filter. This is a numerical optimization method, where the particle filter design process is interpreted into the framework of reinforcement learning by assigning the randomnesses included in both models of the particle filter to the policy of reinforcement learning. In this method, estimation by the particle filter is repeatedly performed and the parameters that determine both models are gradually updated according to the estimation results. The advantage is that it can optimize various objective functions, such as the estimation accuracy of the particle filter, the variance of the particles, the likelihood of the parameters, and the regularization term of the parameters. We derive the conditions to guarantee that the optimization calculation converges with probability 1. Furthermore, in order to show that the proposed method can be applied to practical-scale problems, we design the particle filter for mobile robot localization, which is an essential technology for autonomous navigation. By numerical simulations, it is demonstrated that the proposed method further improves the localization accuracy compared to the conventional method.},
keywords={},
doi={10.1587/transinf.2021EDP7192},
ISSN={1745-1361},
month={May},}
부
TY - JOUR
TI - Particle Filter Design Based on Reinforcement Learning and Its Application to Mobile Robot Localization
T2 - IEICE TRANSACTIONS on Information
SP - 1010
EP - 1023
AU - Ryota YOSHIMURA
AU - Ichiro MARUTA
AU - Kenji FUJIMOTO
AU - Ken SATO
AU - Yusuke KOBAYASHI
PY - 2022
DO - 10.1587/transinf.2021EDP7192
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2022
AB - Particle filters have been widely used for state estimation problems in nonlinear and non-Gaussian systems. Their performance depends on the given system and measurement models, which need to be designed by the user for each target system. This paper proposes a novel method to design these models for a particle filter. This is a numerical optimization method, where the particle filter design process is interpreted into the framework of reinforcement learning by assigning the randomnesses included in both models of the particle filter to the policy of reinforcement learning. In this method, estimation by the particle filter is repeatedly performed and the parameters that determine both models are gradually updated according to the estimation results. The advantage is that it can optimize various objective functions, such as the estimation accuracy of the particle filter, the variance of the particles, the likelihood of the parameters, and the regularization term of the parameters. We derive the conditions to guarantee that the optimization calculation converges with probability 1. Furthermore, in order to show that the proposed method can be applied to practical-scale problems, we design the particle filter for mobile robot localization, which is an essential technology for autonomous navigation. By numerical simulations, it is demonstrated that the proposed method further improves the localization accuracy compared to the conventional method.
ER -