Frontiers in Robotics and AI最新文献

{"title":"A PSO-ML-LSTM-based IMU state estimation approach for manipulator teleoperation.","authors":"Renyi Zhou, Yuanchong Li, Aimin Zhang, Tie Zhang, Yisheng Guan, Zhijia Zhao, Shouyan Chen","doi":"10.3389/frobt.2025.1638853","DOIUrl":"https://doi.org/10.3389/frobt.2025.1638853","url":null,"abstract":"<p><p>Manipulator teleoperation can liberate humans from hazardous tasks. Signal noise caused by environmental disturbances and the devices' inherent characteristics may limit the teleoperation performance. This paper proposes an approach for inertial measurement unit (IMU) state estimation based on particle swarm optimization (PSO) and modulated long short-term memory (ML-LSTM) neural networks to mitigate the impact of IMU cumulative error on the robot teleoperation performance. A motion mapping model for the human arm and a seven-degree-of-freedom (7-DOF) robotic arm are first established based on global configuration parameters and a hybrid mapping method. This model is used to describe the impact of IMU cumulative error on the robot teleoperation performance. Subsequently, the IMU pose state estimation model is constructed using PSO and ML-LSTM neural networks. The initial data of multiple IMUs and handling handles are used for training the estimation model. Finally, comparative experiments are conducted to verify the performance of the proposed state estimation model. The results demonstrate that the PSO-ML-LSTM algorithm can effectively eliminate the impact of IMU cumulative errors on robot teleoperation.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1638853"},"PeriodicalIF":3.0,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12481027/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208004","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}

{"title":"Gödelian embodied self-referential genomic intelligence: lessons for AI and AGI from the genomic blockchain.","authors":"Sheri Markose","doi":"10.3389/frobt.2025.1624695","DOIUrl":"10.3389/frobt.2025.1624695","url":null,"abstract":"<p><p>The security of code-based digital records is a major concern of the 21st century. AI and artificial general intelligence (AGI) can be hacked to pieces by digital adversaries, and some AI objectives can lead to existential threats. The former arises from sitting duck problems that all software systems are vulnerable to, and the latter include control and misalignment problems. Blockchain technology, circa 2009, can address these problems: hashing algorithms rely on a consensus mechanism in manmade software systems to keep early blocks of software immutable and tamper-proof from digital malware, while new blocks can be added only if consistently aligned with original blocks. There is evidence that the ancient precedent of the genomic blockchain, underpinning the unbroken chain of life, uses a self-referential rather than a consensus-based hashing algorithm. Knowledge of self-codes permits biotic elements to achieve a hack-free agenda by self-reporting that they have been \"negated,\" or hacked, exactly implementing the Gödel sentence from foundational mathematics of Gödel, Turing, and Post (G-T-P). This results in an arms race in open-ended novelty to secure the primacy of original self-codes. Selfhood and autonomy are staples of neuroscience on complex self-other social cognition and increasingly of autonomous AGI agents capable of end-to-end programmed self-assembly. My perspective is that self-referential G-T-P information processing, first found in the adaptive immune system of jawed fish 500 mya and more recently in mirror neuron systems of humans, has enabled code-based self-organized intelligent systems like life to survive over 3.7 billion years. Some lessons for AGI can be gleaned from this discussion.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1624695"},"PeriodicalIF":3.0,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12477408/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145201637","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}

{"title":"A comprehensive review and bibliometric analysis on collaborative robotics for industry: safety emerging as a core focus.","authors":"Aida Haghighi, Morteza Cheraghi, Jérôme Pocachard, Valérie Botta-Genoulaz, Sabrina Jocelyn, Hamidreza Pourzarei","doi":"10.3389/frobt.2025.1605682","DOIUrl":"10.3389/frobt.2025.1605682","url":null,"abstract":"<p><p>Research organizations and academics often seek to map the development of scientific fields, identify research gaps, and guide the direction of future research. In cobot-related research, the scientific literature consulted does not propose any comprehensive research agenda. Moreover, cobots, industrial robots inherently designed to collaborate with humans, bring with them emerging issues. To solve them, interdisciplinary research is often essential (e.g., combination of engineering, ergonomics and biomechanics expertise to handle safety challenges). This paper proposes an exhaustive study that employs a scoping review and bibliometric analysis to provide a structured macro perspective on the developments, key topics, and trends in cobot research for industry. A total of 2,195 scientific publications were gained from the Web of Science database, and a thorough selection process narrowed them down to 532 papers for comprehensive analysis. Descriptive statistics were employed to analyze bibliometric measures, highlighting publication trends, leading journals, the most productive institutions, engaged countries, influential authors, and prominent research topics. Co-authorship and bibliographic couplings were also examined. Through a co-occurrence analysis of terms, the content and research objectives of the papers were systematically reviewed and lead to a univocal categorization framework. That categorization can support organizations or researchers in different cobotics (collaborative robotics) fields by understanding research developments and trends, identifying collaboration opportunities, selecting suitable publication venues, advancing the theoretical and experimental understanding of automatic collaborative systems, and identifying research directions and predicting the evolution of publication quantity in cobotics.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1605682"},"PeriodicalIF":3.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12464494/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187143","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}

{"title":"A projection-based inverse kinematic model for extensible continuum robots and hyper-redundant robots with an elbow joint.","authors":"Sven Fritsch, Dirk Oberschmidt","doi":"10.3389/frobt.2025.1627688","DOIUrl":"10.3389/frobt.2025.1627688","url":null,"abstract":"<p><p>Inverse kinematics is a core problem in robotics, involving the use of kinematic equations to calculate the joint configurations required to achieve a target pose. This study introduces a novel inverse kinematic model (IKM) for extensible (i.e., length-adjustable) continuum robots (CRs) and hyper-redundant robots (HRRs) featuring an elbow joint. This IKM numerically solves a set of equations representing geometric constraints (abbreviated as NSGC). NSGC can handle target poses <math> <mrow> <msub><mrow><mi>X</mi></mrow> <mrow><mi>t</mi></mrow> </msub> <mo>=</mo> <mrow><mo>[</mo> <mrow> <msub><mrow><mi>x</mi></mrow> <mrow><mi>t</mi></mrow> </msub> <mo>,</mo> <msub><mrow><mi>y</mi></mrow> <mrow><mi>t</mi></mrow> </msub> <mo>,</mo> <msub><mrow><mi>z</mi></mrow> <mrow><mi>t</mi></mrow> </msub> <mo>,</mo> <msub><mrow><mi>ψ</mi></mrow> <mrow><mi>t</mi></mrow> </msub> </mrow> <mo>]</mo></mrow> </mrow> </math> in 3D space, which are projected onto a 2D plane and solved numerically. NSGC is capable of real-time operation and accounts for elbow joint limits. Extensive simulations and empirical tests confirm the reliability, performance, and practical applicability of NSGC.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1627688"},"PeriodicalIF":3.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12464886/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187126","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}

{"title":"Enhancing weed detection through knowledge distillation and attention mechanism.","authors":"Ali El Alaoui, Hajar Mousannif","doi":"10.3389/frobt.2025.1654074","DOIUrl":"10.3389/frobt.2025.1654074","url":null,"abstract":"<p><p>Weeds pose a significant challenge in agriculture by competing with crops for essential resources, leading to reduced yields. To address this issue, researchers have increasingly adopted advanced machine learning techniques. Recently, Vision Transformers (ViT) have demonstrated remarkable success in various computer vision tasks, making their application to weed classification, detection, and segmentation more advantageous compared to traditional Convolutional Neural Networks (CNNs) due to their self-attention mechanism. However, the deployment of these models in agricultural robotics is hindered by resource limitations. Key challenges include high training costs, the absence of inductive biases, the extensive volume of data required for training, model size, and runtime memory constraints. This study proposes a knowledge distillation-based method for optimizing the ViT model. The approach aims to enhance the ViT model architecture while maintaining its performance for weed detection. To facilitate the training of the compacted ViT student model and enable parameter sharing and local receptive fields, knowledge was distilled from ResNet-50, which serves as the teacher model. Experimental results demonstrate significant enhancements and improvements in the student model, achieving a mean Average Precision (mAP) of 83.47%. Additionally, the model exhibits minimal computational expense, with only 5.7 million parameters. The proposed knowledge distillation framework successfully addresses the computational constraints associated with ViT deployment in agricultural robotics while preserving detection accuracy for weed detection applications.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1654074"},"PeriodicalIF":3.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12460097/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187215","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}

{"title":"Task-specific CNN size reduction through content-specific pruning.","authors":"Nurbek Konyrbaev, Martin Lukac, Sabit Ibadulla, Askhat Diveev, Elena Sofronova, Asem Galymzhankyzy","doi":"10.3389/frobt.2025.1552068","DOIUrl":"10.3389/frobt.2025.1552068","url":null,"abstract":"<p><p>The widespread and growing use of flying unmanned aerial vehicles (UAVs) is attributed to their high spatial mobility, autonomous control, and lower cost compared to usual manned flying vehicles. Applications, such as surveying, searching, or scanning the environment with application-specific sensors, have made extensive use of UAVs in fields like agriculture, geography, forestry, and biology. However, due to the large number of applications and types of UAVs, limited power has to be taken into account when designing task-specific software for a target UAV. In particular, the power constraints of smaller UAVs will generally necessitate reducing power consumption by limiting functionality, decreasing their movement radius, or increasing their level of autonomy. Reducing the overhead of control and decision-making software onboard is one approach to increasing the autonomy of UAVs. Specifically, we can make the onboard control software more efficient and focused on specific tasks, which means it will need less computing power than a general-purpose algorithm. In this work, we focus on reducing the size of the computer vision object classification algorithm. We define different tasks by specifying which objects the UAV must recognize, and we construct a convolutional neural network (CNN) for each specific classification. However, rather than creating a custom CNN that requires its dataset, we begin with a pre-trained general-purpose classifier. We then choose specific groups of objects to recognize, and by using response-based pruning (RBP), we simplify the general-purpose CNN to fit our specific needs. We evaluate the pruned models in various scenarios. The results indicate that the evaluated task-specific pruning can reduce the size of the neural model and increase the accuracy of the classification tasks. For small UAVs intended for tasks with reduced visual content, the proposed method solves both the size reduction and individual model training problems.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1552068"},"PeriodicalIF":3.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12460118/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187210","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}

{"title":"Avoidance behaviours of farmed Atlantic salmon (<i>Salmo salar</i> L.) to artificial sound and light: a case study of net-pen mariculture in Norway.","authors":"Qin Zhang, Nina Bloecher, Linn Danielsen Evjemo, Martin Føre, Eleni Kelasidi","doi":"10.3389/frobt.2025.1657567","DOIUrl":"10.3389/frobt.2025.1657567","url":null,"abstract":"<p><p>Intensive finfish aquaculture is increasingly relying on enabling technologies and solutions such as sensor systems, robotics, and other machinery. Together with conventional farming equipment, these systems may emanate acoustic noise and artificial light, impacting the pen environment. Farmed fish have been observed to respond behaviourally and/or physiologically to anthropogenic sounds and lights, indicating a stress reaction that could impair welfare and health. This study aimed to investigate how farmed Atlantic salmon respond to such stimuli, with direct implications for the design and operation of robotic and mechanised systems in sea pens. We conducted experiments where we systematically exposed adult farmed Atlantic salmon in commercial net pens to sounds of frequencies within the range common to farm equipment (100-1,000 Hz), and submerged lights at 8 and 12 m with four different intensities (600 lx-14,500 lx). Data was analysed using sonar data and a deep learning (DL) based method for processing that automatically identified fish distribution patterns and estimated the average avoidance distance to the sound/light source. The fish fled from the sound source while playing sounds of 400 Hz, while sounds at other frequencies did not elicit a response. The response to light intensity depended on deployment depth, with the fish moving closer to the source when intensity was increased at 8 m depth, but conversely moving further away with increasing density when it was placed at 12 m. These outcomes are important inputs for the design of equipment, autonomous vehicles, robotic interventions and operations at commercial farms to ensure that their sound and light emissions have minimal impact on the fish, thereby reducing the potential of induced stress.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1657567"},"PeriodicalIF":3.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12460098/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187194","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}

{"title":"The impact of hotel robots' service quality on continuance intention: the moderating effect of personal innovation.","authors":"Yaru Shi, Weifang Zhan, Chengpeng Lin","doi":"10.3389/frobt.2025.1667123","DOIUrl":"10.3389/frobt.2025.1667123","url":null,"abstract":"<p><p>As service robots are increasingly integrated into the hotel industry to enhance operational efficiency and customer experience, understanding consumers' responses to robotic services has become a critical research agenda. However, empirical evidence on how customers evaluate the service quality of hotel robots and how these evaluations influence their continuance intention remains limited. Drawing on the SERVQUAL framework, this study redefines service quality in the context of AI-powered hotel robots through five dimensions: reliability, assurance, entertainment, anthropomorphism, and tangibles. Furthermore, the study explores the moderating role of personal innovativeness in the relationship between perceived service quality and continuance intention. Data were collected via an online survey from 400 Generation Z consumers in China who had prior experience with item-delivery robots in hotel settings. The results indicate that assurance, entertainment, anthropomorphism, and tangibles have significant positive effects on continuance intention, while reliability does not show a statistically significant impact. Moreover, personal innovativeness significantly moderates the effects of certain service quality dimensions, suggesting that individual differences in technology readiness shape consumer reactions to robotic services. This study contributes to the literature by extending traditional service quality theory into the domain of human-robot interaction and by highlighting the nuanced mechanisms through which robot-specific service attributes influence user loyalty. Practical implications are offered for hotel managers seeking to optimize robot deployment strategies and improve guest engagement in technology-enhanced service environments.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1667123"},"PeriodicalIF":3.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12457782/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145151331","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}

{"title":"Encouraging classroom activities for children using avatar robots: a field trial.","authors":"Megumi Kawata, Masashi Maeda, Hirokazu Kumazaki, Hiroko Kamide, Jun Baba, Naomi Matsuura, Hiroshi Ishiguro, Yuichiro Yoshikawa","doi":"10.3389/frobt.2025.1571804","DOIUrl":"10.3389/frobt.2025.1571804","url":null,"abstract":"<p><p>Educational institutions are facing a critical shortage of teachers worldwide. Consequently, the trend of introducing interactive robots into educational sites is growing. However, most previous research focused on specific subjects or time slots, and only a few studies have introduced interactive robots to participate in whole classroom activities with children routinely. This study investigates the use of avatar robots operated by multiple remote operators in elementary school classrooms. Over nine days, a 5th-grade class was observed to assess the robot's impact on student engagement, motivation, and peer interactions, and compared to classes where any avatar robots were not introduced. Key findings include improved student confidence in presentations, enhanced social interactions during recess, and positive feedback on the robot's role in supporting classroom activities. The results suggest that avatar robots, with consistent remote operation, can provide valuable educational support without strong negative reactions from students.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1571804"},"PeriodicalIF":3.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12454100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145139115","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}

{"title":"Developing reliability centered maintenance in automotive robotic welding machines for a tier 1 supplier.","authors":"H T O Alaka, K Mpofu, B I Ramatsetse, T A Adegbola, M O Adeoti","doi":"10.3389/frobt.2025.1620370","DOIUrl":"https://doi.org/10.3389/frobt.2025.1620370","url":null,"abstract":"<p><p>The study highlights the effectiveness of FMEA in robotic spot-welding operations, providing a systematic approach to enhancing performance in an automotive assembly line. Robotic welding industries depend on mechanized, programmable tools to automate welding processes, ensuring efficiency, reliability, and effective material handling. In the automotive sector, Tier 1 suppliers utilize robotic welding machines to produce high volumes of welded assemblies, with daily output exceeding 450 units. However, frequent equipment downtime due to maintenance challenges disrupts productivity and impacts customer satisfaction. This study aims to develop a Reliability-Centered Maintenance (RCM) approach for robotic welding industries, optimizing machine uptime, enhancing product quality, and reducing financial losses caused by unexpected failures. A 3-year dataset was analysed to identify the primary causes of downtime and their associated costs. Failure Modes and Effects Analysis (FMEA) was applied to assess failure modes, determine root causes, and calculate Risk Priority Numbers (RPNs), thereby formulating corrective actions to mitigate recurring failures and enhance operational efficiency. Findings revealed that maintenance-related issues accounted for 79% of total downtime, resulting in financial losses of R2,281,508.82 over 3 years. The application of FMEA provided a structured framework for prioritizing critical failure modes and implementing targeted corrective measures to reduce downtime and enhance overall reliability. To sustain high productivity and quality, it is recommended that robotic welding industries adopt proactive maintenance strategies based on FMEA findings. Regular monitoring, predictive maintenance, and workforce training will help minimize machine failures and optimize operational efficiency.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1620370"},"PeriodicalIF":3.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12454091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145139072","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}