Frontiers in Robotics and AI最新文献_第7页

Editorial: Rising stars in field robotics: 2022 社论：现场机器人技术的后起之秀：2022 年

Frontiers in Robotics and AI Pub Date : 2024-02-14 DOI: 10.3389/frobt.2024.1379661

Dimitrios Kanoulas, Shehryar Khattak, Giuseppe Loianno

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Editorial: Rising stars in field robotics: 2022 社论：现场机器人技术的后起之秀：2022 年

Frontiers in Robotics and AI Pub Date : 2024-02-14 DOI: 10.3389/frobt.2024.1379661

Dimitrios Kanoulas, Shehryar Khattak, Giuseppe Loianno

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Editorial: The future of bio-inspired robotics: an early career scientists’ perspective 社论：生物启发机器人学的未来：早期职业科学家的视角

Frontiers in Robotics and AI Pub Date : 2024-02-13 DOI: 10.3389/frobt.2024.1370948

Marcello Calisti, Li Wen

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AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb: a review 基于人工智能的外骨骼辅助下肢康复方法：综述

Frontiers in Robotics and AI Pub Date : 2024-02-09 DOI: 10.3389/frobt.2024.1341580

Omar Coser, C. Tamantini, Paolo Soda, Loredana Zollo

{"title":"AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb: a review","authors":"Omar Coser, C. Tamantini, Paolo Soda, Loredana Zollo","doi":"10.3389/frobt.2024.1341580","DOIUrl":"https://doi.org/10.3389/frobt.2024.1341580","url":null,"abstract":"Over the past few years, there has been a noticeable surge in efforts to design novel tools and approaches that incorporate Artificial Intelligence (AI) into rehabilitation of persons with lower-limb impairments, using robotic exoskeletons. The potential benefits include the ability to implement personalized rehabilitation therapies by leveraging AI for robot control and data analysis, facilitating personalized feedback and guidance. Despite this, there is a current lack of literature review specifically focusing on AI applications in lower-limb rehabilitative robotics. To address this gap, our work aims at performing a review of 37 peer-reviewed papers. This review categorizes selected papers based on robotic application scenarios or AI methodologies. Additionally, it uniquely contributes by providing a detailed summary of input features, AI model performance, enrolled populations, exoskeletal systems used in the validation process, and specific tasks for each paper. The innovative aspect lies in offering a clear understanding of the suitability of different algorithms for specific tasks, intending to guide future developments and support informed decision-making in the realm of lower-limb exoskeleton and AI applications.","PeriodicalId":504612,"journal":{"name":"Frontiers in Robotics and AI","volume":"61 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139849381","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

AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb: a review 基于人工智能的外骨骼辅助下肢康复方法：综述

Frontiers in Robotics and AI Pub Date : 2024-02-09 DOI: 10.3389/frobt.2024.1341580

Omar Coser, C. Tamantini, Paolo Soda, Loredana Zollo

{"title":"AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb: a review","authors":"Omar Coser, C. Tamantini, Paolo Soda, Loredana Zollo","doi":"10.3389/frobt.2024.1341580","DOIUrl":"https://doi.org/10.3389/frobt.2024.1341580","url":null,"abstract":"Over the past few years, there has been a noticeable surge in efforts to design novel tools and approaches that incorporate Artificial Intelligence (AI) into rehabilitation of persons with lower-limb impairments, using robotic exoskeletons. The potential benefits include the ability to implement personalized rehabilitation therapies by leveraging AI for robot control and data analysis, facilitating personalized feedback and guidance. Despite this, there is a current lack of literature review specifically focusing on AI applications in lower-limb rehabilitative robotics. To address this gap, our work aims at performing a review of 37 peer-reviewed papers. This review categorizes selected papers based on robotic application scenarios or AI methodologies. Additionally, it uniquely contributes by providing a detailed summary of input features, AI model performance, enrolled populations, exoskeletal systems used in the validation process, and specific tasks for each paper. The innovative aspect lies in offering a clear understanding of the suitability of different algorithms for specific tasks, intending to guide future developments and support informed decision-making in the realm of lower-limb exoskeleton and AI applications.","PeriodicalId":504612,"journal":{"name":"Frontiers in Robotics and AI","volume":" 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139789570","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

Development of an earthworm-based soft robot for colon sampling 开发基于蚯蚓的结肠取样软机器人

Frontiers in Robotics and AI Pub Date : 2024-02-07 DOI: 10.3389/frobt.2024.1309220

Gongxin Li, Wei Qiu, Mindong Wang, Yazhou Zhu, Fei Liu

{"title":"Development of an earthworm-based soft robot for colon sampling","authors":"Gongxin Li, Wei Qiu, Mindong Wang, Yazhou Zhu, Fei Liu","doi":"10.3389/frobt.2024.1309220","DOIUrl":"https://doi.org/10.3389/frobt.2024.1309220","url":null,"abstract":"Colorectal cancer as a major disease that poses a serious threat to human health continues to rise in incidence. And the timely colon examinations are crucial for the prevention, diagnosis, and treatment of this disease. Clinically, gastroscopy is used as a universal means of examination, prevention and diagnosis of this disease, but this detection method is not patient-friendly and can easily cause damage to the intestinal mucosa. Soft robots as an emerging technology offer a promising approach to examining, diagnosing, and treating intestinal diseases due to their high flexibility and patient-friendly interaction. However, existing research on intestinal soft robots mainly focuses on controlled movement and observation within the colon or colon-like environments, lacking additional functionalities such as sample collection from the intestine. Here, we designed and developed an earthworm-like soft robot specifically for colon sampling. It consists of a robot body with an earthworm-like structure for movement in the narrow and soft pipe-environments, and a sampling part with a flexible arm structure resembling an elephant trunk for bidirectional bending sampling. This soft robot is capable of flexible movement and sample collection within an colon-like environment. By successfully demonstrating the feasibility of utilizing soft robots for colon sampling, this work introduces a novel method for non-destructive inspection and sampling in the colon. It represents a significant advancement in the field of medical robotics, offering a potential solution for more efficient and accurate examination and diagnosis of intestinal diseases, specifically for colorectal cancer.","PeriodicalId":504612,"journal":{"name":"Frontiers in Robotics and AI","volume":"6 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139796352","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

Building causal models for finding actual causes of unmanned aerial vehicle failures 建立因果模型，找出无人驾驶飞行器故障的实际原因

Frontiers in Robotics and AI Pub Date : 2024-02-07 DOI: 10.3389/frobt.2024.1123762

Ehsan Zibaei, Robin Borth

{"title":"Building causal models for finding actual causes of unmanned aerial vehicle failures","authors":"Ehsan Zibaei, Robin Borth","doi":"10.3389/frobt.2024.1123762","DOIUrl":"https://doi.org/10.3389/frobt.2024.1123762","url":null,"abstract":"Finding actual causes of unmanned aerial vehicle (UAV) failures can be split into two main tasks: building causal models and performing actual causality analysis (ACA) over them. While there are available solutions in the literature to perform ACA, building comprehensive causal models is still an open problem. The expensive and time-consuming process of building such models, typically performed manually by domain experts, has hindered the widespread application of causality-based diagnosis solutions in practice. This study proposes a methodology based on natural language processing for automating causal model generation for UAVs. After collecting textual data from online resources, causal keywords are identified in sentences. Next, cause–effect phrases are extracted from sentences based on predefined dependency rules between tokens. Finally, the extracted cause–effect pairs are merged to form a causal graph, which we then use for ACA. To demonstrate the applicability of our framework, we scrape online text resources of Ardupilot, an open-source UAV controller software. Our evaluations using real flight logs show that the generated graphs can successfully be used to find the actual causes of unwanted events. Moreover, our hybrid cause–effect extraction module performs better than a purely deep-learning based tool (i.e., CiRA) by 32% in precision and 25% in recall in our Ardupilot use case.","PeriodicalId":504612,"journal":{"name":"Frontiers in Robotics and AI","volume":"51 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139797692","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

Building causal models for finding actual causes of unmanned aerial vehicle failures 建立因果模型，找出无人驾驶飞行器故障的实际原因

Frontiers in Robotics and AI Pub Date : 2024-02-07 DOI: 10.3389/frobt.2024.1123762

Ehsan Zibaei, Robin Borth

{"title":"Building causal models for finding actual causes of unmanned aerial vehicle failures","authors":"Ehsan Zibaei, Robin Borth","doi":"10.3389/frobt.2024.1123762","DOIUrl":"https://doi.org/10.3389/frobt.2024.1123762","url":null,"abstract":"Finding actual causes of unmanned aerial vehicle (UAV) failures can be split into two main tasks: building causal models and performing actual causality analysis (ACA) over them. While there are available solutions in the literature to perform ACA, building comprehensive causal models is still an open problem. The expensive and time-consuming process of building such models, typically performed manually by domain experts, has hindered the widespread application of causality-based diagnosis solutions in practice. This study proposes a methodology based on natural language processing for automating causal model generation for UAVs. After collecting textual data from online resources, causal keywords are identified in sentences. Next, cause–effect phrases are extracted from sentences based on predefined dependency rules between tokens. Finally, the extracted cause–effect pairs are merged to form a causal graph, which we then use for ACA. To demonstrate the applicability of our framework, we scrape online text resources of Ardupilot, an open-source UAV controller software. Our evaluations using real flight logs show that the generated graphs can successfully be used to find the actual causes of unwanted events. Moreover, our hybrid cause–effect extraction module performs better than a purely deep-learning based tool (i.e., CiRA) by 32% in precision and 25% in recall in our Ardupilot use case.","PeriodicalId":504612,"journal":{"name":"Frontiers in Robotics and AI","volume":"283 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139857355","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

Development of an earthworm-based soft robot for colon sampling 开发基于蚯蚓的结肠取样软机器人

Frontiers in Robotics and AI Pub Date : 2024-02-07 DOI: 10.3389/frobt.2024.1309220

Gongxin Li, Wei Qiu, Mindong Wang, Yazhou Zhu, Fei Liu

{"title":"Development of an earthworm-based soft robot for colon sampling","authors":"Gongxin Li, Wei Qiu, Mindong Wang, Yazhou Zhu, Fei Liu","doi":"10.3389/frobt.2024.1309220","DOIUrl":"https://doi.org/10.3389/frobt.2024.1309220","url":null,"abstract":"Colorectal cancer as a major disease that poses a serious threat to human health continues to rise in incidence. And the timely colon examinations are crucial for the prevention, diagnosis, and treatment of this disease. Clinically, gastroscopy is used as a universal means of examination, prevention and diagnosis of this disease, but this detection method is not patient-friendly and can easily cause damage to the intestinal mucosa. Soft robots as an emerging technology offer a promising approach to examining, diagnosing, and treating intestinal diseases due to their high flexibility and patient-friendly interaction. However, existing research on intestinal soft robots mainly focuses on controlled movement and observation within the colon or colon-like environments, lacking additional functionalities such as sample collection from the intestine. Here, we designed and developed an earthworm-like soft robot specifically for colon sampling. It consists of a robot body with an earthworm-like structure for movement in the narrow and soft pipe-environments, and a sampling part with a flexible arm structure resembling an elephant trunk for bidirectional bending sampling. This soft robot is capable of flexible movement and sample collection within an colon-like environment. By successfully demonstrating the feasibility of utilizing soft robots for colon sampling, this work introduces a novel method for non-destructive inspection and sampling in the colon. It represents a significant advancement in the field of medical robotics, offering a potential solution for more efficient and accurate examination and diagnosis of intestinal diseases, specifically for colorectal cancer.","PeriodicalId":504612,"journal":{"name":"Frontiers in Robotics and AI","volume":"25 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139855924","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

Active learning strategies for robotic tactile texture recognition tasks 机器人触觉纹理识别任务的主动学习策略

Frontiers in Robotics and AI Pub Date : 2024-02-06 DOI: 10.3389/frobt.2024.1281060

Shemonto Das, Vinicius Prado da Fonseca, Amilcar Soares

{"title":"Active learning strategies for robotic tactile texture recognition tasks","authors":"Shemonto Das, Vinicius Prado da Fonseca, Amilcar Soares","doi":"10.3389/frobt.2024.1281060","DOIUrl":"https://doi.org/10.3389/frobt.2024.1281060","url":null,"abstract":"Accurate texture classification empowers robots to improve their perception and comprehension of the environment, enabling informed decision-making and appropriate responses to diverse materials and surfaces. Still, there are challenges for texture classification regarding the vast amount of time series data generated from robots’ sensors. For instance, robots are anticipated to leverage human feedback during interactions with the environment, particularly in cases of misclassification or uncertainty. With the diversity of objects and textures in daily activities, Active Learning (AL) can be employed to minimize the number of samples the robot needs to request from humans, streamlining the learning process. In the present work, we use AL to select the most informative samples for annotation, thus reducing the human labeling effort required to achieve high performance for classifying textures. We also use a sliding window strategy for extracting features from the sensor’s time series used in our experiments. Our multi-class dataset (e.g., 12 textures) challenges traditional AL strategies since standard techniques cannot control the number of instances per class selected to be labeled. Therefore, we propose a novel class-balancing instance selection algorithm that we integrate with standard AL strategies. Moreover, we evaluate the effect of sliding windows of two-time intervals (3 and 6 s) on our AL Strategies. Finally, we analyze in our experiments the performance of AL strategies, with and without the balancing algorithm, regarding f1-score, and positive effects are observed in terms of performance when using our proposed data pipeline. Our results show that the training data can be reduced to 70% using an AL strategy regardless of the machine learning model and reach, and in many cases, surpass a baseline performance. Finally, exploring the textures with a 6-s window achieves the best performance, and using either Extra Trees produces an average f1-score of 90.21% in the texture classification data set.","PeriodicalId":504612,"journal":{"name":"Frontiers in Robotics and AI","volume":"9 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139798623","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