研究者詳細 - 戸田　雄一郎

論文 - 戸田　雄一郎

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Multilayer Batch Learning Growing Neural Gas for Learning Multiscale Topologies 査読

Yuichiro Toda, Takayuki Matsuno, Mamoru Minami

Journal of Advanced Computational Intelligence and Intelligent Informatics 25 ( 6 ) 1011 - 1023 2021年11月

担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Fuji Technology Press Ltd.

Hierarchical topological structure learning methods are expected to be developed in the field of data mining for extracting multiscale topological structures from an unknown dataset. However, most methods require user-defined parameters, and it is difficult for users to determine these parameters and effectively utilize the method. In this paper, we propose a new parameter-less hierarchical topological structure learning method based on growing neural gas (GNG). First, we propose batch learning GNG (BL-GNG) to improve the learning convergence and reduce the user-designed parameters in GNG. BL-GNG uses an objective function based on fuzzy C-means to improve the learning convergence. Next, we propose multilayer BL-GNG (MBL-GNG), which is a parameter-less unsupervised learning algorithm based on hierarchical topological structure learning. In MBL-GNG, the input data of each layer uses parent nodes to learn more abstract topological structures from the dataset. Furthermore, MBL-GNG can automatically determine the number of nodes and layers according to the data distribution. Finally, we conducted several experiments to evaluate our proposed method by comparing it with other hierarchical approaches and discuss the effectiveness of our proposed method.

DOI： 10.20965/jaciii.2021.p1011

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Growing Neural Gasに基づく環境のトポロジカルマップの構築と未知環境における経路計画査読

戸田雄一郎, 宮瀬光梨, 岩朝睦美, 和田亮雅, 竹田宗馬, 松野隆幸, 久保田直行, 見浪護

知能と情報 33 ( 4 ) 872 - 884 2021年11月

複数属性から構成される特徴ベクトルにおけるGrowing Neural Gasに基づく空間構造の学習査読

戸田雄一郎, 和田亮雅, 松野隆幸, 見浪護

計測自動制御学会論文集 57 ( 4 ) 209 - 218 2021年4月

水中ロボットの海底充電システム構築のための海流外乱適応型嵌合システム査読

戸田雄一郎, 山下耕平, 門田拓也, Hsu Horng-Yi, 齊藤和裕, 見浪護

日本船舶海洋工学会論文集 32 2021年

水中ロボットの自動嵌合制御のための発光次元マーカーを用いた光環境適応特性を有する3次元空間認識手法査読

戸田雄一郎, 中村翔, Hsu Horng-Yi, 見浪護

日本船舶海洋工学会論文集 32 2021年

A cause of natural arm-swing in bipedal walking. 査読

Yuichiro Toda, Ying Wang, Mamoru Minami

Artif. Life Robotics 26 ( 1 ) 76 - 83 2021年

担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：SPRINGER

The research of humanoid is widely discussed whether by simulations or real machines. In human bipedal walking, swinging arms in opposite directions is a natural movement. In this research, a model of the humanoid robot, including slipping, bumping, surface-contacting and point-contacting of the foot has been established, and its dynamical equation is derived by the Newton-Euler method. And the natural arm-swing simulation has been produced, which showed that the input torque in yaw rotation of the torso could cause natural arm-swing. "Natural" means that the arm-swinging motion is induced by coupling effects existing in nonlinear dynamics of humanoid robot even though no torques have been input into shoulders. Based on the results, a hypothesis that the vibration in the yaw rotation of the torso caused natural arm swing is proposed. In this paper, we compared the arm-swing movement with or without the input torque of yaw rotation of the torso by using the above humanoid robot model. The simulation data proved the hypothesis to be valid.

DOI： 10.1007/s10015-020-00636-0

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Stereo-vision-based AUV docking system for resetting the Inertial Navigation System errors 査読

Horng-Yi Hsu, Yuichiro Toda, Kohei Yamashita, Keigo Watanabe, Mamoru Minami

26th International Symposium on Artificial Life and Robotics 2021年

Fitness Calculation with a FPGA Implementation 査読

Shiqian Luo, Yuichiro Toda, Mamoru Minami

26th International Symposium on Artificial Life and Robotics 2021年

Expanding the recognition distance using the Model-based Matching method and the 2D model by zoom cameras 査読

Siyu Pan, Renya Takahashi, Jincheng Li, Yuichiro Toda, Mamoru Minami

26th International Symposium on Artificial Life and Robotics 2021年

Multi-Scale Batch-Learning Growing Neural Gas for Topological Feature Extraction in Navigation of Mobility Support Robots 査読

Mutsumi Iwasa, Naoyuki Kubota, Yuichiro Toda

The 7th International Workshop on Advanced Computational Intelligence and Intelligent Informatics 2021年

A New Concept of Pose Estimation of Arbitrary 3D Object without Prerequisite Knowledge: Projection-based 3D Perception 査読

Yejun Kou, Yuichiro Toda, Mamoru Minami

26th International Symposium on Artificial Life and Robotics 2021年

発光型3次元マーカーの濁度耐性の検証とその有効性確認のための実海域ドッキング実験査読

戸田雄一郎, 向田直樹, 許弘毅, 見浪護

日本船舶海洋工学会論文集 31 145 - 161 2020年

Adaptive evolution strategy sample consensus for 3D reconstruction from two cameras. 査読

Yuichiro Toda, Hsu Horng Yz, Takayuki Matsuno, Mamoru Minami, Dalin Zhou

Artif. Life Robotics 25 ( 3 ) 466 - 474 2020年

担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：SPRINGER

RANdom SAmple Consensus (RANSAC) has been applied to many 3D image processing problems such as homography matrix estimation problems and shape detection from 3D point clouds, and is one of the most popular robust estimator methods. However, RANSAC has a problem related to the trade-off between computational cost and stability of search because RANSAC is based on random sampling. In our previous work, we proposed Adaptive Evolution Strategy SAmple Consensus (A-ESSAC) as a new robust estimator, and we applied ESSAC to the homography matrix estimation for 3D SLAM using RGB-D camera. A-ESSAC is based on Evolution Strategy to maintain the genetic diversity. Furthermore, ESSAC has two heuristic searches. One is a search range control for reducing the computational cost of RANSAC. The other is adaptive/self-adaptive mutation for changing the search strategy of A-ESSAC according to the best fitness value. In this paper, we apply A-ESSAC to 3D reconstruction method using two cameras, and we show an experimental result, and discuss the effectiveness of the proposed method.

DOI： 10.1007/s10015-020-00603-9

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Visibility improvement in relation to turbidity and distance, and application to docking. 査読

Horng-Yi Hsu, Yuichiro Toda, Keigo Watanabe, Mamoru Minami

Artif. Life Robotics 25 ( 3 ) 453 - 465 2020年

3D Simultaneous Localization and Mapping for AUV by RealTime 3-D Perception 査読

Yuya Okada, Yuichiro Toda, Yoshiki Kanda, Mamoru Minami

25th International Symposium on Artificial Life and Robotics 2020年

The Cause of Natural Arm-swing in Bipedal Walking 査読

Ying Wang, Yuichiro Toda, Mamoru Minami

25th International Symposium on Artificial Life and Robotics 2020年

Intelligent Control for Illuminance Measurement by an Autonomous Mobile Robot 査読

R. Inoue, T. Arai, Y. Toda, M. Tsujimoto, K. Taniguchi, N. Kubota

Proceedings of IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO 2019-October 270 - 274 2019年10月

掲載種別：研究論文（国際会議プロシーディングス）

Over the last decade, the labor shortage suffers various industry sectors due to declining birthrate and aging population. Thus, autonomous robots have been widely researched for overcoming the labor-shortage phenomena. The construction sector is one of the industries that require high manpower to conduct various tasks. Traditional illuminance measurement in construction sites is one of the tasks that require much manpower and consumption time. As such, the development of various robots has been conducted to autonomously measure construction sites' illuminance. In this paper, we develop an autonomous mobile robot to perform illuminance measurement while performing simultaneous localization and mapping (SLAM), and obstacle avoidance. Human operators first set a navigation path and send it to the robot. With the given path, the robot starts to navigate the environment autonomously and measures the illuminance of the target points in the environment. The robot is validated through several experiments conducted in real-world indoor environments. Experimental results showed that the robot was able to autonomously measure the illuminance of the environment.

DOI： 10.1109/ARSO46408.2019.8948806

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Experimental verification of turbidity tolerance of stereo-vision-based 3D pose estimation system 査読

Myo Myint, Khin Nwe Lwin, Naoki Mukada, Daiki Yamada, Takayuki Matsuno, Yuuichirou Toda, Saitou Kazuhiro, Mamoru Minami

Journal of Marine Science and Technology 24 ( 3 ) 756 - 779 2019年9月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Springer Science and Business Media LLC

This paper presents the verification of the turbidity tolerance of a stereo-vision-based 3D pose estimation system for underwater docking applications. To the best of the authors' knowledge, no studies have yet been conducted on 3D pose (position and orientation) estimation against turbidity for underwater vehicles. Therefore, the effect of turbidity on the 3D pose estimation performance of underwater vehicles and a method of operating under turbid conditions were studied in this work. A 3D pose estimation method using the real-time multi-step genetic algorithm (RM-GA) proposed by the authors in the previous works shows robust pose estimation performance against changing environmental conditions. This paper discusses how and why the RM-GA is well suited to effective 3D pose estimation, even when turbid conditions disturb visual servoing. The experimental results confirm the performance of the proposed 3D pose estimation system under different levels of turbidity. To demonstrate the practical usefulness of the RM-GA, docking experiments were conducted in a turbid pool and a real sea environment to verify the performance and tolerance of the proposed system under turbid conditions. The experimental results verify the robustness of the system against turbidity, presenting a possible solution to a major problem in the field of robotics.

DOI： 10.1007/s00773-018-0586-7

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その他リンク： http://link.springer.com/article/10.1007/s00773-018-0586-7/fulltext.html

Behavior Acquisition on a Mobile Robot Using Reinforcement Learning with Continuous State Space 査読

Tomoyuki Arai, Yuichiro Toda, Naoyuki Kubota

Proceedings - International Conference on Machine Learning and Cybernetics 2019-July 458 - 461 2019年7月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：IEEE

In the application of Reinforcement Learning to real tasks, the construction of state space is a significant problem. In order to use in the real-world environment, we need to deal with the problem of continuous information. Therefore, we proposed a method of the construction of state space using Growing Neural Gas. In our method, the agent constructs a state space model from its own experience autonomously. Furthermore, it can reconstruct the suitable state space model to adapt the complication of the environment. Through the experiments, we showed that Reinforcement Learning could be performed efficiently by successively updating the state space model according to the environment.

DOI： 10.1109/ICMLC48188.2019.8949181

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Human Posture Recognition for Estimation of Human Body Condition 査読

Wei Quan, Jinseok Woo, Yuichiro Toda, Naoyuki Kubota

JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 23 ( 3 ) 519 - 527 2019年5月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：FUJI TECHNOLOGY PRESS LTD

Human posture recognition has been a popular research topic since the development of the referent fields of human-robot interaction, and simulation operation. Most of these methods are based on supervised learning, and a large amount of training information is required to conduct an ideal assessment. In this study, we propose a solution to this by applying a number of unsupervised learning algorithms based on the forward kinematics model of the human skeleton. Next, we optimize the proposed method by integrating particle swarm optimization (PSO) for optimization. The advantage of the proposed method is no pre-training data is that required for human posture generation and recognition. We validate the method by conducting a series of experiments with human subjects.

DOI： 10.20965/jaciii.2019.p0519

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