Frontiers in Neurorobotics最新文献_第6页

Improved object detection method for unmanned driving based on Transformers 基于变压器的改进型无人驾驶物体检测方法

IF 3.1 4区计算机科学

Frontiers in Neurorobotics Pub Date : 2024-05-01 DOI: 10.3389/fnbot.2024.1342126

Huaqi Zhao, Xiang Peng, Su Wang, Jun-Bao Li, Jeng-Shyang Pan, Xiaoguang Su, Xiaomin Liu

{"title":"Improved object detection method for unmanned driving based on Transformers","authors":"Huaqi Zhao, Xiang Peng, Su Wang, Jun-Bao Li, Jeng-Shyang Pan, Xiaoguang Su, Xiaomin Liu","doi":"10.3389/fnbot.2024.1342126","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1342126","url":null,"abstract":"The object detection method serves as the core technology within the unmanned driving perception module, extensively employed for detecting vehicles, pedestrians, traffic signs, and various objects. However, existing object detection methods still encounter three challenges in intricate unmanned driving scenarios: unsatisfactory performance in multi-scale object detection, inadequate accuracy in detecting small objects, and occurrences of false positives and missed detections in densely occluded environments. Therefore, this study proposes an improved object detection method for unmanned driving, leveraging Transformer architecture to address these challenges. First, a multi-scale Transformer feature extraction method integrated with channel attention is used to enhance the network's capability in extracting features across different scales. Second, a training method incorporating Query Denoising with Gaussian decay was employed to enhance the network's proficiency in learning representations of small objects. Third, a hybrid matching method combining Optimal Transport and Hungarian algorithms was used to facilitate the matching process between predicted and actual values, thereby enriching the network with more informative positive sample features. Experimental evaluations conducted on datasets including KITTI demonstrate that the proposed method achieves 3% higher mean Average Precision (mAP) than that of the existing methodologies.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"107 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140841101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The meta-learning method for the ensemble model based on situational meta-task 基于情景元任务的集合模型元学习方法

IF 3.1 4区计算机科学

Frontiers in Neurorobotics Pub Date : 2024-04-26 DOI: 10.3389/fnbot.2024.1391247

Zhengchao Zhang, Lianke Zhou, Yuyang Wu, Nianbin Wang

{"title":"The meta-learning method for the ensemble model based on situational meta-task","authors":"Zhengchao Zhang, Lianke Zhou, Yuyang Wu, Nianbin Wang","doi":"10.3389/fnbot.2024.1391247","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1391247","url":null,"abstract":"IntroductionThe meta-learning methods have been widely used to solve the problem of few-shot learning. Generally, meta-learners are trained on a variety of tasks and then generalized to novel tasks.MethodsHowever, existing meta-learning methods do not consider the relationship between meta-tasks and novel tasks during the meta-training period, so that initial models of the meta-learner provide less useful meta-knowledge for the novel tasks. This leads to a weak generalization ability on novel tasks. Meanwhile, different initial models contain different meta-knowledge, which leads to certain differences in the learning effect of novel tasks during the meta-testing period. Therefore, this article puts forward a meta-optimization method based on situational meta-task construction and cooperation of multiple initial models. First, during the meta-training period, a method of constructing situational meta-task is proposed, and the selected candidate task sets provide more effective meta-knowledge for novel tasks. Then, during the meta-testing period, an ensemble model method based on meta-optimization is proposed to minimize the loss of inter-model cooperation in prediction, so that multiple models cooperation can realize the learning of novel tasks.ResultsThe above-mentioned methods are applied to popular few-shot character datasets and image recognition datasets. Furthermore, the experiment results indicate that the proposed method achieves good effects in few-shot classification tasks.DiscussionIn future work, we will extend our methods to provide more generalized and useful meta-knowledge to the model during the meta-training period when the novel few-shot tasks are completely invisible.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"35 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140806225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ontology based autonomous robot task processing framework 基于本体的自主机器人任务处理框架

IF 3.1 4区计算机科学

Frontiers in Neurorobotics Pub Date : 2024-04-23 DOI: 10.3389/fnbot.2024.1401075

Yueguang Ge, Shaolin Zhang, Yinghao Cai, Tao Lu, Haitao Wang, Xiaolong Hui, Shuo Wang

{"title":"Ontology based autonomous robot task processing framework","authors":"Yueguang Ge, Shaolin Zhang, Yinghao Cai, Tao Lu, Haitao Wang, Xiaolong Hui, Shuo Wang","doi":"10.3389/fnbot.2024.1401075","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1401075","url":null,"abstract":"IntroductionIn recent years, the perceptual capabilities of robots have been significantly enhanced. However, the task execution of the robots still lacks adaptive capabilities in unstructured and dynamic environments.MethodsIn this paper, we propose an ontology based autonomous robot task processing framework (ARTProF), to improve the robot's adaptability within unstructured and dynamic environments. ARTProF unifies ontological knowledge representation, reasoning, and autonomous task planning and execution into a single framework. The interface between the knowledge base and neural network-based object detection is first introduced in ARTProF to improve the robot's perception capabilities. A knowledge-driven manipulation operator based on Robot Operating System (ROS) is then designed to facilitate the interaction between the knowledge base and the robot's primitive actions. Additionally, an operation similarity model is proposed to endow the robot with the ability to generalize to novel objects. Finally, a dynamic task planning algorithm, leveraging ontological knowledge, equips the robot with adaptability to execute tasks in unstructured and dynamic environments.ResultsExperimental results on real-world scenarios and simulations demonstrate the effectiveness and efficiency of the proposed ARTProF framework.DiscussionIn future work, we will focus on refining the ARTProF framework by integrating neurosymbolic inference.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"115 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Residual learning-based robotic image analysis model for low-voltage distributed photovoltaic fault identification and positioning 基于残差学习的机器人图像分析模型，用于低压分布式光伏故障识别和定位

IF 3.1 4区计算机科学

Frontiers in Neurorobotics Pub Date : 2024-04-22 DOI: 10.3389/fnbot.2024.1396979

Xudong Zhang, Yunlong Ge, Yifeng Wang, Jun Wang, Wenhao Wang, Lijun Lu

{"title":"Residual learning-based robotic image analysis model for low-voltage distributed photovoltaic fault identification and positioning","authors":"Xudong Zhang, Yunlong Ge, Yifeng Wang, Jun Wang, Wenhao Wang, Lijun Lu","doi":"10.3389/fnbot.2024.1396979","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1396979","url":null,"abstract":"With the fast development of large-scale Photovoltaic (PV) plants, the automatic PV fault identification and positioning have become an important task for the PV intelligent systems, aiming to guarantee the safety, reliability, and productivity of large-scale PV plants. In this paper, we propose a residual learning-based robotic (UAV) image analysis model for low-voltage distributed PV fault identification and positioning. In our target scenario, the unmanned aerial vehicles (UAVs) are deployed to acquire moving images of low-voltage distributed PV power plants. To get desired robustness and accuracy of PV image detection, we integrate residual learning with attention mechanism into the UAV image analysis model based on you only look once v4 (YOLOv4) network. Then, we design the sophisticated multi-scale spatial pyramid fusion and use it to optimize the YOLOv4 network for the nuanced task of fault localization within PV arrays, where the Complete-IOU loss is incorporated in the predictive modeling phase, significantly enhancing the accuracy and efficiency of fault detection. A series of experimental comparisons in terms of the accuracy of fault positioning are conducted, and the experimental results verify the feasibility and effectiveness of the proposed model in dealing with the safety and reliability maintenance of low-voltage distributed PV systems.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"19 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140635325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid knowledge transfer for MARL based on action advising and experience sharing 基于行动建议和经验分享的混合型 MARL 知识转让

IF 3.1 4区计算机科学

Frontiers in Neurorobotics Pub Date : 2024-04-10 DOI: 10.3389/fnbot.2024.1364587

Feng Liu, Dongqi Li, Jian Gao

{"title":"Hybrid knowledge transfer for MARL based on action advising and experience sharing","authors":"Feng Liu, Dongqi Li, Jian Gao","doi":"10.3389/fnbot.2024.1364587","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1364587","url":null,"abstract":"Multiagent Reinforcement Learning (MARL) has been well adopted due to its exceptional ability to solve multiagent decision-making problems. To further enhance learning efficiency, knowledge transfer algorithms have been developed, among which experience-sharing-based and action-advising-based transfer strategies share the mainstream. However, it is notable that, although there exist many successful applications of both strategies, they are not flawless. For the long-developed action-advising-based methods (namely KT-AA, short for knowledge transfer based on action advising), their data efficiency and scalability are not satisfactory. As for the newly proposed experience-sharing-based knowledge transfer methods (KT-ES), although the shortcomings of KT-AA have been partially overcome, they are incompetent to correct specific bad decisions in the later learning stage. To leverage the superiority of both KT-AA and KT-ES, this study proposes KT-Hybrid, a hybrid knowledge transfer approach. In the early learning phase, KT-ES methods are employed, expecting better data efficiency from KT-ES to enhance the policy to a basic level as soon as possible. Later, we focus on correcting specific errors made by the basic policy, trying to use KT-AA methods to further improve the performance. Simulations demonstrate that the proposed KT-Hybrid outperforms well-received action-advising- and experience-sharing-based methods.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"14 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rail surface defect data enhancement method based on improved ACGAN 基于改进 ACGAN 的铁路表面缺陷数据增强方法

IF 3.1 4区计算机科学

Frontiers in Neurorobotics Pub Date : 2024-04-09 DOI: 10.3389/fnbot.2024.1397369

He Zhendong, Gao Xiangyang, Liu Zhiyuan, An Xiaoyu, Zheng Anping

引用次数: 0

Extended residual learning with one-shot imitation learning for robotic assembly in semi-structured environment 针对半结构化环境中机器人装配的一次模仿学习与扩展残差学习

IF 3.1 4区计算机科学

Frontiers in Neurorobotics Pub Date : 2024-04-08 DOI: 10.3389/fnbot.2024.1355170

Chuang Wang, Chupeng Su, Baozheng Sun, Gang Chen, Longhan Xie

{"title":"Extended residual learning with one-shot imitation learning for robotic assembly in semi-structured environment","authors":"Chuang Wang, Chupeng Su, Baozheng Sun, Gang Chen, Longhan Xie","doi":"10.3389/fnbot.2024.1355170","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1355170","url":null,"abstract":"IntroductionRobotic assembly tasks require precise manipulation and coordination, often necessitating advanced learning techniques to achieve efficient and effective performance. While residual reinforcement learning with a base policy has shown promise in this domain, existing base policy approaches often rely on hand-designed full-state features and policies or extensive demonstrations, limiting their applicability in semi-structured environments.MethodsIn this study, we propose an innovative Object-Embodiment-Centric Imitation and Residual Reinforcement Learning (OEC-IRRL) approach that leverages an object-embodiment-centric (OEC) task representation to integrate vision models with imitation and residual learning. By utilizing a single demonstration and minimizing interactions with the environment, our method aims to enhance learning efficiency and effectiveness. The proposed method involves three key steps: creating an object-embodiment-centric task representation, employing imitation learning for a base policy using via-point movement primitives for generalization to different settings, and utilizing residual RL for uncertainty-aware policy refinement during the assembly phase.ResultsThrough a series of comprehensive experiments, we investigate the impact of the OEC task representation on base and residual policy learning and demonstrate the effectiveness of the method in semi-structured environments. Our results indicate that the approach, requiring only a single demonstration and less than 1.2 h of interaction, improves success rates by 46% and reduces assembly time by 25%.DiscussionThis research presents a promising avenue for robotic assembly tasks, providing a viable solution without the need for specialized expertise or custom fixtures.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"36 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140810365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization method for human-robot command combinations of hexapod robot based on multi-objective constraints 基于多目标约束的六足机器人人机指令组合优化方法

IF 3.1 4区计算机科学

Frontiers in Neurorobotics Pub Date : 2024-04-05 DOI: 10.3389/fnbot.2024.1393738

Xiaolei Chen, Bo You, Zheng Dong

{"title":"Optimization method for human-robot command combinations of hexapod robot based on multi-objective constraints","authors":"Xiaolei Chen, Bo You, Zheng Dong","doi":"10.3389/fnbot.2024.1393738","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1393738","url":null,"abstract":"Due to the heavy burden on human drivers when remotely controlling hexapod robots in complex terrain environments, there is a critical need for robot intelligence to assist in generating control commands. Therefore, this study proposes a mapping process framework that generates a combination of human-robot commands based on decision target values, focusing on the task of robot intelligence assisting drivers in generating human-robot command combinations. Furthermore, human-robot state constraints are quantified as geometric constraints on robot motion and driver fatigue constraints. By optimizing and filtering the feasible set of human-robot commands based on human-robot state constraints, instruction combinations are formed and recommended to the driver in real-time, thereby enhancing the efficiency and safety of human-machine coordination. To validate the effectiveness of the proposed method, a remote human-robot collaborative driving control system based on wearable devices is designed and implemented. Experimental results demonstrate that drivers utilizing the human-robot command recommendation system exhibit significantly improved robot walking stability and reduced collision rates compared to individual driving.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"9 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140583468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The application prospects of robot pose estimation technology: exploring new directions based on YOLOv8-ApexNet 机器人姿态估计技术的应用前景：基于 YOLOv8-ApexNet 的新方向探索

IF 3.1 4区计算机科学

Frontiers in Neurorobotics Pub Date : 2024-04-05 DOI: 10.3389/fnbot.2024.1374385

XianFeng Tang, Shuwei Zhao

{"title":"The application prospects of robot pose estimation technology: exploring new directions based on YOLOv8-ApexNet","authors":"XianFeng Tang, Shuwei Zhao","doi":"10.3389/fnbot.2024.1374385","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1374385","url":null,"abstract":"IntroductionService robot technology is increasingly gaining prominence in the field of artificial intelligence. However, persistent limitations continue to impede its widespread implementation. In this regard, human motion pose estimation emerges as a crucial challenge necessary for enhancing the perceptual and decision-making capacities of service robots.MethodThis paper introduces a groundbreaking model, YOLOv8-ApexNet, which integrates advanced technologies, including Bidirectional Routing Attention (BRA) and Generalized Feature Pyramid Network (GFPN). BRA facilitates the capture of inter-keypoint correlations within dynamic environments by introducing a bidirectional information propagation mechanism. Furthermore, GFPN adeptly extracts and integrates feature information across different scales, enabling the model to make more precise predictions for targets of various sizes and shapes.ResultsEmpirical research findings reveal significant performance enhancements of the YOLOv8-ApexNet model across the COCO and MPII datasets. Compared to existing methodologies, the model demonstrates pronounced advantages in keypoint localization accuracy and robustness.DiscussionThe significance of this research lies in providing an efficient and accurate solution tailored for the realm of service robotics, effectively mitigating the deficiencies inherent in current approaches. By bolstering the accuracy of perception and decision-making, our endeavors unequivocally endorse the widespread integration of service robots within practical applications.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"2 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140584727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

3D human pose data augmentation using Generative Adversarial Networks for robotic-assisted movement quality assessment 利用生成式对抗网络增强三维人体姿态数据，用于机器人辅助运动质量评估

IF 3.1 4区计算机科学

Frontiers in Neurorobotics Pub Date : 2024-04-05 DOI: 10.3389/fnbot.2024.1371385

Xuefeng Wang, Yang Mi, Xiang Zhang

{"title":"3D human pose data augmentation using Generative Adversarial Networks for robotic-assisted movement quality assessment","authors":"Xuefeng Wang, Yang Mi, Xiang Zhang","doi":"10.3389/fnbot.2024.1371385","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1371385","url":null,"abstract":"In the realm of human motion recognition systems, the augmentation of 3D human pose data plays a pivotal role in enriching and enhancing the quality of original datasets through the generation of synthetic data. This augmentation is vital for addressing the current research gaps in diversity and complexity, particularly when dealing with rare or complex human movements. Our study introduces a groundbreaking approach employing Generative Adversarial Networks (GANs), coupled with Support Vector Machine (SVM) and DenseNet, further enhanced by robot-assisted technology to improve the precision and efficiency of data collection. The GANs in our model are responsible for generating highly realistic and diverse 3D human motion data, while SVM aids in the effective classification of this data. DenseNet is utilized for the extraction of key features, facilitating a comprehensive and integrated approach that significantly elevates both the data augmentation process and the model's ability to process and analyze complex human movements. The experimental outcomes underscore our model's exceptional performance in motion quality assessment, showcasing a substantial improvement over traditional methods in terms of classification accuracy and data processing efficiency. These results validate the effectiveness of our integrated network model, setting a solid foundation for future advancements in the field. Our research not only introduces innovative methodologies for 3D human pose data enhancement but also provides substantial technical support for practical applications across various domains, including sports science, rehabilitation medicine, and virtual reality. By combining advanced algorithmic strategies with robotic technologies, our work addresses key challenges in data augmentation and motion quality assessment, paving the way for new research and development opportunities in these critical areas.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"49 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140583345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0