Artificial Life and Robotics最新文献

Research on coordinated control strategy of distributed static synchronous series compensator based on multi-objective optimization immune algorithm 基于多目标优化免疫算法的分布式静态同步串联补偿器协调控制策略研究

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Artificial Life and Robotics Pub Date : 2024-10-15 DOI: 10.1007/s10015-024-00967-2

Yu Wang, Zhenzhong Yan, Liting Yan, Xufei Liu, Yanpeng Liu

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AI robots pioneer the Smarter Inclusive Society 人工智能机器人开创更智能的包容性社会

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Artificial Life and Robotics Pub Date : 2024-10-15 DOI: 10.1007/s10015-024-00975-2

Yasuhisa Hirata

{"title":"AI robots pioneer the Smarter Inclusive Society","authors":"Yasuhisa Hirata","doi":"10.1007/s10015-024-00975-2","DOIUrl":"10.1007/s10015-024-00975-2","url":null,"abstract":"<div><p>This paper outlines a project aimed at realizing a “Smarter Inclusive Society” by 2050 through the integration of AI robots into various public facilities. Led by the Cabinet Office’s “Moonshot Research and Development Program,” the project focuses on developing Adaptable AI-enabled Robots that enhance self-efficacy by supporting users’ abilities while maintaining their sense of independence. Key to the project is the Robotic Nimbus, a soft and flexible robot designed to provide tailored assistance while preserving user agency. The concept of “Adaptable AI-enabled Robots” is introduced to ensure versatility in accommodating user needs and preferences. In addition to physical assistance, the project emphasizes creating engaging experiences through activities like dance and sports, fostering excitement and inclusivity. Collaborations, such as the “Yes We Dance!” performance, demonstrate the potential of AI technology in enhancing rehabilitation opportunities and promoting social participation. By 2050, the project aims to establish a society where AI robots contribute to mental, physical, and social wellbeing, empowering individuals to engage in independent activities and fostering a vibrant, inclusive community. This paper is a compilation of articles/papers/presentations previously presented on the Moonshot Hirata project.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10015-024-00975-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Probabilistic model for high-level intention estimation and trajectory prediction in urban environments 城市环境中高层次意图估计和轨迹预测的概率模型

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Artificial Life and Robotics Pub Date : 2024-10-08 DOI: 10.1007/s10015-024-00973-4

Yunsoo Bok, Naoki Suganuma, Keisuke Yoneda

{"title":"Probabilistic model for high-level intention estimation and trajectory prediction in urban environments","authors":"Yunsoo Bok, Naoki Suganuma, Keisuke Yoneda","doi":"10.1007/s10015-024-00973-4","DOIUrl":"10.1007/s10015-024-00973-4","url":null,"abstract":"<div><p>To enable successful automated driving, precise behavior prediction of surrounding vehicles is indispensable in urban traffic scenarios. Furthermore, given that a vehicle’s behavior is influenced by the movements of other road users, it becomes crucial to estimate their intentions to anticipate precise future motion. However, the elevated complexity resulting from interdependencies among traffic participants and the uncertainty arising from the object recognition errors present additional challenges. Despite extensive research on inferring intentions, many studies have concentrated on estimating intentions from interactions, resulting in a lack of practicality in urban traffic environments due to low computational efficiency and low robustness against recognition failure of strongly interacting road users. In this paper, we introduce a practical stochastic model for intention estimation and trajectory prediction of surrounding vehicles in automated driving under urban traffic environments. The trajectory is forecasted based on hierarchically computed and probabilistically estimated intentions, which represent an interpretation of vehicle behavior, utilizing only the kinematic state of the focal vehicle and HD maps to ensure real-time performance and enhance robustness. The evaluated results demonstrate that the proposed model surpasses straightforward methods in terms of accuracy while maintaining computational efficiency and exhibits robustness against the recognition failure of traffic participants which strongly influence the focal vehicle.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10015-024-00973-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Preservation of emotional context in tweet embeddings on social networking sites 在社交网站的推文嵌入中保留情感语境

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Artificial Life and Robotics Pub Date : 2024-10-08 DOI: 10.1007/s10015-024-00974-3

Osamu Maruyama, Asato Yoshinaga, Ken-ichi Sawai

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Spiking neural networks-based generation of caterpillar-like soft robot crawling motions 基于尖峰神经网络生成类似毛毛虫的软机器人爬行动作

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Artificial Life and Robotics Pub Date : 2024-10-04 DOI: 10.1007/s10015-024-00970-7

SeanKein Yoshioka, Takahiro Iwata, Yuki Maruyama, Daisuke Miki

{"title":"Spiking neural networks-based generation of caterpillar-like soft robot crawling motions","authors":"SeanKein Yoshioka, Takahiro Iwata, Yuki Maruyama, Daisuke Miki","doi":"10.1007/s10015-024-00970-7","DOIUrl":"10.1007/s10015-024-00970-7","url":null,"abstract":"<div><p>Robots have been widely used in daily life in recent years. Unlike conventional robots made of rigid materials, soft robots utilize stretchable and flexible materials, allowing flexible movements similar to those of living organisms, which are difficult for traditional robots. Previous studies have used periodic signals to control soft robots, which lead to repetitive motions and make it challenging to generate environment-adapted motions. To address this issue, control methods can be learned through deep reinforcement learning to enable soft robots to select appropriate actions based on observations, improving their adaptability to environmental changes. In addition, as mobile robots have limited onboard resources, it is necessary to conserve battery consumption and achieve low-power control. Therefore, the use of spiking neural networks (SNNs) with neuromorphic chips enables low-power control of soft robots. In this study, we investigated the learning methods for SNNs aimed at controlling soft robots. Experiments were conducted using a caterpillar-like soft robot model based on previous studies, and the effectiveness of the learning method was evaluated.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10015-024-00970-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Real-time drowsiness evaluation system using marker-less facial motion capture 使用无标记面部动作捕捉的实时嗜睡评估系统

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Artificial Life and Robotics Pub Date : 2024-10-03 DOI: 10.1007/s10015-024-00972-5

Yudai Koshi, Hisaya Tanaka

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A two-stage image segmentation method for harvest order decision of wood ear mushroom 决定木耳采收顺序的两阶段图像分割方法

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Artificial Life and Robotics Pub Date : 2024-10-01 DOI: 10.1007/s10015-024-00971-6

Kazuya Okamura, Ryo Matsumura, Hironori Kitakaze

{"title":"A two-stage image segmentation method for harvest order decision of wood ear mushroom","authors":"Kazuya Okamura, Ryo Matsumura, Hironori Kitakaze","doi":"10.1007/s10015-024-00971-6","DOIUrl":"10.1007/s10015-024-00971-6","url":null,"abstract":"<div><p>This study proposes a method for determining the appropriate harvesting order for densely growing wood ear mushrooms by recognizing their growth stages and harvesting priorities from depth images obtained from a stereo camera. We aim to minimize crop damage and improve the quality of harvested crops during the harvesting of densely growing crops using a robot arm. The proposed two-stage method consists of two models—one of the models to recognize priority harvest regions, and the other model to identify individual wood ear mushroom regions and growth stages. The final harvesting order is determined based on the outputs of these models. The models were trained using simulated CGI data of wood ear mushroom growth. The experimental results show that the appropriate harvesting order can be outputted in 57.5% of the cases for the 40 sets of test data. The results show that it is possible to determine the harvesting order of dense wood ear mushrooms based solely on depth images. However, there is still room for improvement in operations in actual environments. Further work is needed to enhance the method’s robustness and accuracy.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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A digital hardware system for real-time biorealistic stimulation on in vitro cardiomyocytes 对体外心肌细胞进行实时生物仿真刺激的数字硬件系统

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Artificial Life and Robotics Pub Date : 2024-09-30 DOI: 10.1007/s10015-024-00968-1

Pierre-Marie Faure, Agnès Tixier-Mita, Timothée Levi

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Biomimetic snake locomotion using central pattern generators network and bio-hybrid robot perspective 从中央模式发生器网络和生物混合机器人的角度看仿生蛇运动

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Artificial Life and Robotics Pub Date : 2024-09-25 DOI: 10.1007/s10015-024-00969-0

Jérémy Cheslet, Romain Beaubois, Tomoya Duenki, Farad Khoyratee, Takashi Kohno, Yoshiho Ikeuchi, Timothée Lévi

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A comparative study of linear and nonlinear regression models for blood glucose estimation based on near-infrared facial images from 760 to 1650 nm wavelength 基于波长为 760 至 1650 纳米的近红外面部图像的线性和非线性血糖估计回归模型比较研究

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Artificial Life and Robotics Pub Date : 2024-09-20 DOI: 10.1007/s10015-024-00961-8

Mayuko Nakagawa, Kosuke Oiwa, Yasushi Nanai, Kent Nagumo, Akio Nozawa

{"title":"A comparative study of linear and nonlinear regression models for blood glucose estimation based on near-infrared facial images from 760 to 1650 nm wavelength","authors":"Mayuko Nakagawa, Kosuke Oiwa, Yasushi Nanai, Kent Nagumo, Akio Nozawa","doi":"10.1007/s10015-024-00961-8","DOIUrl":"10.1007/s10015-024-00961-8","url":null,"abstract":"<div><p>We have attempted to estimate blood glucose levels based on facial images measured in the near-infrared band, which is highly biopermeable, to establish a remote minimally invasive blood glucose measurement method. We measured facial images in the near-infrared wavelength range of 760–1650 nm, and constructed a general model for blood glucose level estimation by linear regression using the weights of spatial features of the measured facial images as explanatory variables. The results showed that the accuracy values of blood glucose estimation in the generalization performance evaluation were 43.02 mg/dL for NIR-I (760–1100 nm) and 43.61 mg/dL for NIR-II (1050–1650 nm) in the RMSE of the general model. Since biological information is nonlinear, it is necessary to explore suitable modeling methods for blood glucose estimation, including not only linear regression but also nonlinear regression. The purpose of this study is to explore suitable regression methods among linear and nonlinear regression methods to construct a blood glucose estimation model based on facial images with wavelengths from 760 to 1650 nm. The results showed that model using Random Forest had the best estimation accuracy with an RMSE of 36.02 mg/dL in NIR-I and the MR model had the best estimation accuracy with RMSE of 36.70 mg/dL in NIR-II under the current number of subjects and measurement data points. The independent components selected for the model have spatial features considered to be simply individual differences that are not related to blood glucose variation.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142519068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0