2024/04/19 更新

モリモト ダイチ
森本 大智
MORIMOTO Daich
Scopus 論文情報  
総論文数: 0  総Citation: 0  h-index: 2

Citation Countは当該年に発表した論文の被引用数

所属
大学院工学研究院 機械知能工学研究系
職名
助教
外部リンク

取得学位

  • 広島大学  -  博士(工学)   2023年09月

学内職務経歴

  • 2024年04月 - 現在   九州工業大学   大学院工学研究院   機械知能工学研究系     助教

学外略歴

  • 2023年10月 - 2024年03月   日本学術振興会   日本学術振興会特別研究員   日本国

論文

  • Generating collective behavior of a robotic swarm using an attention agent with deep neuroevolution 査読有り 国際誌

    Iwami A., Morimoto D., Shiozaki N., Hiraga M., Ohkura K.

    Artificial Life and Robotics   28 ( 4 )   669 - 679   2023年11月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)

    This paper focuses on generating collective behavior of a robotic swarm using an attention agent. The selective attention mechanism enables an agent to cope with environmental variations which are irrelevant to the task. This paper applies attention mechanisms to a robotic swarm for enhancing system-level properties, such as flexibility or scalability. To train an attention agent, evolutionary computations become a promising method, because a controller structure is not restricted by a gradient-based method. Therefore, this paper employs a deep neuroevolution approach to generating collective behavior in a robotic swarm. The experiments are conducted by computer simulations that consist of the Unity 3D game engine. The performance of the attention agent is compared with the convolutional neural network approach. The experimental results showed that the attention agent obtained generalization abilities in a robotic swarm similar to single-agent problems.

    DOI: 10.1007/s10015-023-00902-x

    Scopus

    その他リンク: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85173787793&origin=inward

  • An evolutionary robotics approach to a multi-legged robotic swarm in a rough terrain environment 査読有り 国際誌

    Morimoto D., Tsukamoto H., Hiraga M., Ohkura K., Munetomo M.

    Artificial Life and Robotics   28 ( 4 )   661 - 668   2023年11月

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    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)

    This paper demonstrates a controller design of a multi-legged robotic swarm in a rough terrain environment. Many studies in swarm robotics are conducted with mobile robots that work in relatively flat fields. This paper focuses on a multi-legged robotic swarm, which is expected to operate not only in a flat field but also in rough terrain environments. However, designing a robot controller becomes a challenging problem because a designer has to consider how to coordinate a large number of joints in a robot, besides the complexity of a swarm problem. This paper employed an evolutionary robotics approach for the automatic design of a robot controller. The experiments were conducted by computer simulations with the path formation task. The results showed that the proposed approach succeeds in generating collective behavior in flat and rough terrain environments.

    DOI: 10.1007/s10015-023-00906-7

    Scopus

    その他リンク: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85174576051&origin=inward

  • When Less Is More in Embodied Evolution: Robotic Swarms Have Better Evolvability with Constrained Communication 査読有り 国際誌

    Hiraga M., Morimoto D., Katada Y., Ohkura K.

    Journal of Robotics and Mechatronics   35 ( 4 )   988 - 996   2023年08月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)

    Embodied evolution is an evolutionary robotics approach that implements an evolutionary algorithm over a population of robots and evolves while the robots perform their tasks. In embodied evolution, robots send and receive genomes from their neighbors and generate an offspring genome from the exchanged genomes. This study focused on the effects of the communication range for exchanging genomes on the evolvability of embodied evolution. Experiments were conducted using computer simulations, where robot controllers were evolved during a two-target navigation task. The results of the experiments showed that the robotic swarm could achieve better performance by reducing the communication range for exchanging genomes.

    DOI: 10.20965/jrm.2023.p0988

    Scopus

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  • Generating Collective Behavior of a Multi-Legged Robotic Swarm Using Deep Reinforcement Learning 査読有り 国際誌

    Morimoto D., Iwamoto Y., Hiraga M., Ohkura K.

    Journal of Robotics and Mechatronics   35 ( 4 )   977 - 987   2023年08月

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    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)

    This paper presents a method of generating collective behavior of a multi-legged robotic swarm using deep reinforcement learning. Most studies in swarm robotics have used mobile robots driven by wheels. These robots can operate only on relatively flat sur-faces. In this study, a multi-legged robotic swarm was employed to generate collective behavior not only on a flat field but also on rough terrain fields. However, designing a controller for a multi-legged robotic swarm becomes a challenging problem because it has a large number of actuators than wheeled-mobile robots. This paper applied deep reinforcement learning to designing a controller. The proximal policy optimization (PPO) algorithm was utilized to train the robot con-troller. The controller was trained through the task that required robots to walk and form a line. The results of computer simulations showed that the PPO led to the successful design of controllers for a multi-legged robotic swarm in flat and rough terrains.

    DOI: 10.20965/jrm.2023.p0977

    Scopus

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  • Generating collective behavior of a multi-legged robotic swarm using an evolutionary robotics approach 査読有り 国際誌

    Morimoto D., Hiraga M., Shiozaki N., Ohkura K., Munetomo M.

    Artificial Life and Robotics   27 ( 4 )   751 - 760   2022年11月

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    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)

    This paper demonstrates to generate a collective behavior of a multi-legged robotic swarm based on the evolutionary robotics approach. Most studies in swarm robotics are conducted using mobile robots driven by wheels. This paper focuses on generating collective behavior using a multi-legged robotic swarm. The evolutionary robotics approach is employed for designing a robot controller. The intuition-based constraint factors are incorporated into the fitness function to make the gait of robots similar to natural organisms. The experiment on a task of forming a line is conducted in computer simulations using the PyBullet physics engine. The robot controller is represented by a recurrent neural network with a single hidden layer. The experimental results show that proposed constraint factors successfully designed the robot’s gait similar to natural organisms. The results also show that the evolutionary robotics approach successfully designed the robot controller for collective behavior of a multi-legged robotic swarm.

    DOI: 10.1007/s10015-022-00800-8

    Scopus

    その他リンク: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85139623502&origin=inward

  • Evolving collective step-climbing behavior in multi-legged robotic swarm 査読有り 国際誌

    Morimoto D., Hiraga M., Shiozaki N., Ohkura K., Munetomo M.

    Artificial Life and Robotics   27 ( 2 )   333 - 340   2022年05月

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    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)

    This paper focuses on generating the collective step-climbing behavior of a multi-legged robotic swarm. Most studies on swarm robotics develop collective behaviors in a flat environment using mobile robots equipped with wheels. However, these types of robots could only show relatively simple behavior, which limits a task that could be addressed by a robotic swarm. This paper deals with a step-climbing task, in which a robotic swarm climbs a step that is too high for a single robot. The robots have to use other robots as a foothold to achieve the task. To generate such three-dimensional behavior, a robotic swarm is conducted using the multi-legged robot inspired by ants. The robot controller is obtained by the combination of the neuroevolution approach with manual designed methods. The results of the computer simulations show that the designed controller successfully achieve the step-climbing task.

    DOI: 10.1007/s10015-021-00725-8

    Scopus

    その他リンク: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85122309720&origin=inward

  • Generating and Analyzing Collective Step-Climbing Behavior in a Multi-legged Robotic Swarm 査読有り 国際誌

    Morimoto D., Hiraga M., Ohkura K., Munetomo M.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   13491 LNCS   324 - 331   2022年01月

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    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(国際会議プロシーディングス)

    This paper focuses on generating and analyzing collective step-climbing behavior in a multi-legged robotic swarm. The multi-legged robotic swarm is expected to climb obstacles that are hard for a single robot by using other robots as stepping stones. However, designing a robot controller for a multi-legged robotic swarm becomes a challenging problem because it designs not only a gait for the basic movement of robots but also the behavior of robots to exhibit collective behavior. This paper employs the evolutionary robotics (ER) approach for designing a robot controller that consists of a recurrent neural network. The controllers are evaluated in the collective step-climbing task conducted by computer simulations. The results show that the ER approach successfully designed the robot gait to achieve the task. Additionally, the results of the analysis confirm that the robot obtained the actions to support other robots along with climbing other robots.

    DOI: 10.1007/978-3-031-20176-9_29

    Scopus

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