Artificial Life and Robotics最新文献_第2页

Multi-objective path integral policy improvement for learning robotic motion 机器人运动学习的多目标路径积分策略改进

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Artificial Life and Robotics Pub Date : 2025-05-02 DOI: 10.1007/s10015-025-01027-z

Hayato Sago, Ryo Ariizumi, Toru Asai, Shun-ichi Azuma

引用次数: 0

Performance improvement of Ear-EEG SSVEP-BCI using reliability score 采用信度评分法改进耳- eeg SSVEP-BCI的性能

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Artificial Life and Robotics Pub Date : 2025-04-30 DOI: 10.1007/s10015-025-01025-1

Sodai Kondo, Hideyuki Harafuji, Ren Kiuchi, Asahi Saito, Kakeru Tanaka, Wataru Wakayama, Hisaya Tanaka

{"title":"Performance improvement of Ear-EEG SSVEP-BCI using reliability score","authors":"Sodai Kondo, Hideyuki Harafuji, Ren Kiuchi, Asahi Saito, Kakeru Tanaka, Wataru Wakayama, Hisaya Tanaka","doi":"10.1007/s10015-025-01025-1","DOIUrl":"10.1007/s10015-025-01025-1","url":null,"abstract":"<div><p>Steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) are known for high speed, accuracy, and multivalue input. Integrating ear-electroencephalogram (EEG) can make SSVEP-BCI more accessible for everyday use. This study introduces a reliability score to enhance the performance of ear-EEG SSVEP-BCI by dynamically adjusting measurement duration and enabling asynchronous detection. Two analysis methods, learning canonical correlation analysis (LCCA) and task-related component analysis, were evaluated. Using the reliability score, the accuracy for ear-EEG SSVEP-BCI reached <span>(100)</span>% with an information transfer rate (ITR) of <span>(22.36pm 3.54)</span> bits/min, compared to <span>(61.93pm 9.22)</span>% accuracy and <span>(15.32pm 4.59)</span> bits/min ITR without the reliability score. These findings demonstrate that the reliability score significantly improves ear-EEG SSVEP-BCI performance, suggesting its potential to enhance usability in practical applications. </p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"30 3","pages":"449 - 457"},"PeriodicalIF":0.8,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10015-025-01025-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145171455","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

Collision avoidance control of multiple UAVs using collision cones and control barrier functions 基于碰撞锥和控制障碍函数的多无人机避碰控制

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Artificial Life and Robotics Pub Date : 2025-04-25 DOI: 10.1007/s10015-025-01020-6

Thiviyathinesvaran Palani, Supuni Wijesundera, Hiroaki Fukushima

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Multi-objective optimization of flight schedules to maximize constraint tolerance by local search and archive mechanisms 基于局部搜索和存档机制的航班调度多目标优化

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Artificial Life and Robotics Pub Date : 2025-04-20 DOI: 10.1007/s10015-025-01021-5

Tomoki Ishizuka, Akinori Murata, Hiroyuki Sato, Keiki Takadama

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Stable dynamic patterns generated by retrograde model 由逆行模型生成的稳定动态模式

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Artificial Life and Robotics Pub Date : 2025-04-07 DOI: 10.1007/s10015-025-01017-1

Mari Nakamura

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Towards flexible swarms: comparison of flocking models with varying complexity 走向灵活的群体：具有不同复杂性的群集模型的比较

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Artificial Life and Robotics Pub Date : 2025-04-07 DOI: 10.1007/s10015-025-01016-2

Lauritz Keysberg, Naoki Wakamiya

{"title":"Towards flexible swarms: comparison of flocking models with varying complexity","authors":"Lauritz Keysberg, Naoki Wakamiya","doi":"10.1007/s10015-025-01016-2","DOIUrl":"10.1007/s10015-025-01016-2","url":null,"abstract":"<div><p>One remarkable feat of biological swarms is their ability to work under very different environmental circumstances and disturbances. They exhibit a flexible kind of robustness, which accommodates external events without staying on rigid positions. Based on the observation that conventionally robust flocking models can be very complex and use information unavailable to biological swarm, we undertook a wide investigation into the properties of existing flocking models such as Boid, Couzin, Vicsek, and Cucker–Smale. That is, to see if a similar “natural” flexibility could be observed in flocking models with lower complexity. We established a toolset of three metrics which allows for a comprehensive evaluation of different flocking models. These metrics measure general model performance, robustness under noise, as well as a naive complexity of the model itself. Our results show a general trend for divergence between performance and robustness. The most robust models had a medium–high complexity. While our results show no clear relation between robustness and low complexity, we discuss examples for robust behavior with simple rules.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"30 2","pages":"219 - 226"},"PeriodicalIF":0.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10015-025-01016-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925571","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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6DOF localization with AUKF based on triple RTK-GNSS 基于三重RTK-GNSS的AUKF 6DOF定位

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Artificial Life and Robotics Pub Date : 2025-04-04 DOI: 10.1007/s10015-025-01018-0

Takahiro Shimizu, Shoichi Maeyama

{"title":"6DOF localization with AUKF based on triple RTK-GNSS","authors":"Takahiro Shimizu, Shoichi Maeyama","doi":"10.1007/s10015-025-01018-0","DOIUrl":"10.1007/s10015-025-01018-0","url":null,"abstract":"<div><p>SLAM (simultaneous localization and mapping) plays a crucial role in autonomous navigation. In a previous study, SLAM based on AUKF (augmented unscented Kalman filter), called AUKF-SLAM, was proposed. This study demonstrated that simultaneous estimation of kinematic parameters improves the accuracy on 2D SLAM in an indoor environment. We currently aim to develop the 3D AUKF-SLAM for outdoor use, and this paper presents the 6DOF localization based on AUKF as a preliminary step. To expand 2D (3DOF) localization to 6DOF localization, we adopted quaternion for attitude representation. However, it is not the best way to estimate each element of the four-dimensional vector of quaternion as state variables because they do not vary independently. As a solution to this problem, the idea of estimating the attitude error represented as the three-dimensional parameter called GRPs (generalized Rodrigues parameters) was proposed. In addition, it was reported that simultaneous estimation of the attitude error represented as GRPs and states not represented as errors is effective for the estimation of the motion of space crafts. Therefore, we applied this method to wheeled mobile robots to address the problem and realize the 6DOF localization based on AUKF. We implemented this system on ROS (robot operating system) and experimented in simulational and real environments. As a result, we demonstrated that it could perform 6DOF localization and estimation of the wheel radius simultaneously.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"30 3","pages":"493 - 501"},"PeriodicalIF":0.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161656","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

Discrimination of stress coping responses on dimensionality-reduced facial thermal image space 降维人脸热图像空间应力应对响应的识别

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Artificial Life and Robotics Pub Date : 2025-04-03 DOI: 10.1007/s10015-025-01022-4

Masahito Takano, Shiori Oyama, Kent Nagumo, Akio Nozawa

引用次数: 0

Neuroevolution for vision-based seeking behavior in 3D soft voxel robots 三维软体素机器人基于视觉的搜索行为的神经进化

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Artificial Life and Robotics Pub Date : 2025-03-29 DOI: 10.1007/s10015-025-01019-z

Christian Hahm

{"title":"Neuroevolution for vision-based seeking behavior in 3D soft voxel robots","authors":"Christian Hahm","doi":"10.1007/s10015-025-01019-z","DOIUrl":"10.1007/s10015-025-01019-z","url":null,"abstract":"<div><p>This paper details a simple experiment that tests two genetic encodings, NEAT and HyperNEAT, for the evolution of vision-based food-seeking behavior in neural-controlled 3D soft voxel robots. The evolution of food-seeking behavior is a preliminary step towards ecosystems of advanced artificial animals, in which the animals seek both food and mates. Two environments were tested: with and without deadly obstacles. Traditional evolutionary search was used, with an objective-based fitness function. Both NEAT and HyperNEAT encodings were tested for the evolution of robot neural controllers. The results of the experiment showed the NEAT encoding resulted in increasingly effective food-seeking behavior over time, whereas experiments with the HyperNEAT encoding did not achieve the desired behavior. This suggests that NEAT at least is a viable algorithm to evolve neural networks for the task of vision-based object-seeking in complex robots, and warrants further experimentation. On the other hand, HyperNEAT struggled with this task. This could be due to a number of reasons, including a common issue like EA being stuck in local optima, or because the encoding might struggle to evolve and represent irregular structures required for the task.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"30 3","pages":"502 - 511"},"PeriodicalIF":0.8,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10015-025-01019-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145171559","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

Estimation of floc condition in a dewatering process by image analysis using machine learning 利用机器学习的图像分析估计脱水过程中的絮体状态

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Artificial Life and Robotics Pub Date : 2025-03-24 DOI: 10.1007/s10015-025-01014-4

Atsuki Fukasawa, Shinya Watanabe

{"title":"Estimation of floc condition in a dewatering process by image analysis using machine learning","authors":"Atsuki Fukasawa, Shinya Watanabe","doi":"10.1007/s10015-025-01014-4","DOIUrl":"10.1007/s10015-025-01014-4","url":null,"abstract":"<div><p>Dewatering is a crucial process in sludge treatment plants, and appropriate mixing of polymer and sludge is an important factor in achieving efficient dewatering. This study focused on the condition of flocs produced by mixing sludge and polymer, and estimated the floc condition through visual analysis of images. In this study, the estimation of floc condition was assumed to be a classification problem of mixer speed, and validation was conducted to classify the appropriate speed based on the images. The proposed methodology involved the development of a machine learning model characterized by high accuracy and transparency. This model was formulated using two features extracted from the images, i.e., the gaps between flocs and their texture, which are the parameters used by human operators to estimate floc condition. Explainable Boosting Machine was used as the machine learning model, which allows interpretation of the model’s contents and can be applied easily. The classification accuracy of this model was validated using both interpolated and extrapolated data, yielding accuracies exceeding 95% in both scenarios. Furthermore, comparative analysis was performed between the proposed transparent box model and a conventional Convolutional Neural Network (CNN) model. Despite its transparent box nature, the proposed approach demonstrated a comparable level of accuracy to the CNN model in this comparative study.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"30 3","pages":"439 - 448"},"PeriodicalIF":0.8,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145168380","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