Frontiers in Neurorobotics最新文献

{"title":"I-BaR: integrated balance rehabilitation framework","authors":"Tugce Ersoy, Pınar Kaya, Elif Hocaoglu, Ramazan Unal","doi":"10.3389/fnbot.2024.1401931","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1401931","url":null,"abstract":"Neurological diseases are observed in approximately 1 billion people worldwide. A further increase is foreseen at the global level as a result of population growth and aging. Individuals with neurological disorders often experience cognitive, motor, sensory, and lower extremity dysfunctions. Thus, the possibility of falling and balance problems arise due to the postural control deficiencies that occur as a result of the deterioration in the integration of multi-sensory information. We propose a novel rehabilitation framework, Integrated Balance Rehabilitation (I-BaR), to improve the effectiveness of the rehabilitation with objective assessment, individualized therapy, convenience with different disability levels and adoption of assist-as-needed paradigm and, with integrated rehabilitation process as whole, that is, ankle-foot preparation, balance, and stepping phases, respectively. Integrated Balance Rehabilitation allows patients to improve their balance ability by providing multi-modal feedback: visual via utilization of virtual reality; vestibular via anteroposterior and mediolateral perturbations with the robotic platform; proprioceptive via haptic feedback.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"49 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141532147","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}

{"title":"EMG-YOLO: road crack detection algorithm for edge computing devices","authors":"Yan Xing, Xu Han, Xiaodong Pan, Dong An, Weidong Liu, Yuanshen Bai","doi":"10.3389/fnbot.2024.1423738","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1423738","url":null,"abstract":"IntroductionRoad cracks significantly shorten the service life of roads. Manual detection methods are inefficient and costly. The YOLOv5 model has made some progress in road crack detection. However, issues arise when deployed on edge computing devices. The main problem is that edge computing devices are directly connected to sensors. This results in the collection of noisy, poor-quality data. This problem adds computational burden to the model, potentially impacting its accuracy. To address these issues, this paper proposes a novel road crack detection algorithm named EMG-YOLO.MethodsFirst, an Efficient Decoupled Header is introduced in YOLOv5 to optimize the head structure. This approach separates the classification task from the localization task. Each task can then focus on learning its most relevant features. This significantly reduces the model’s computational resources and time. It also achieves faster convergence rates. Second, the IOU loss function in the model is upgraded to the MPDIOU loss function. This function works by minimizing the top-left and bottom-right point distances between the predicted bounding box and the actual labeled bounding box. The MPDIOU loss function addresses the complex computation and high computational burden of the current YOLOv5 model. Finally, the GCC3 module replaces the traditional convolution. It performs global context modeling with the input feature map to obtain global context information. This enhances the model’s detection capabilities on edge computing devices.ResultsExperimental results show that the improved model has better performance in all parameter indicators compared to current mainstream algorithms. The EMG-YOLO model improves the accuracy of the YOLOv5 model by 2.7%. The mAP (0.5) and mAP (0.9) are improved by 2.9% and 0.9%, respectively. The new algorithm also outperforms the YOLOv5 model in complex environments on edge computing devices.DiscussionThe EMG-YOLO algorithm proposed in this paper effectively addresses the issues of poor data quality and high computational burden on edge computing devices. This is achieved through optimizing the model head structure, upgrading the loss function, and introducing global context modeling. Experimental results demonstrate significant improvements in both accuracy and efficiency, especially in complex environments. Future research can further optimize this algorithm and explore more lightweight and efficient object detection models for edge computing devices.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"29 17 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501150","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}

{"title":"The transmission line foreign body detection algorithm based on weighted spatial attention","authors":"Yuanyuan Wang, Haiyang Tian, Tongtong Yin, Zhaoyu Song, Abdullahi Suleiman Hauwa, Haiyan Zhang, Shangbing Gao, Liguo Zhou","doi":"10.3389/fnbot.2024.1424158","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1424158","url":null,"abstract":"IntroductionThe secure operation of electric power transmission lines is essential for the economy and society. However, external factors such as plastic film and kites can cause damage to the lines, potentially leading to power outages. Traditional detection methods are inefficient, and the accuracy of automated systems is limited in complex background environments.MethodsThis paper introduces a Weighted Spatial Attention (WSA) network model to address the low accuracy in identifying extraneous materials within electrical transmission infrastructure due to background texture occlusion. Initially, in the model preprocessing stage, color space conversion, image enhancement, and improved Large Selective Kernel Network (LSKNet) technology are utilized to enhance the model's proficiency in detecting foreign objects in intricate surroundings. Subsequently, in the feature extraction stage, the model adopts the dynamic sparse BiLevel Spatial Attention Module (BSAM) structure proposed in this paper to accurately capture and identify the characteristic information of foreign objects in power lines. In the feature pyramid stage, by replacing the feature pyramid network structure and allocating reasonable weights to the Bidirectional Feature Pyramid Network (BiFPN), the feature fusion results are optimized, ensuring that the semantic information of foreign objects in the power line output by the network is effectively identified and processed.ResultsThe experimental outcomes reveal that the test recognition accuracy of the proposed WSA model on the PL (power line) dataset has improved by three percentage points compared to that of the YOLOv8 model, reaching 97.6%. This enhancement demonstrates the WSA model's superior capability in detecting foreign objects on power lines, even in complex environmental backgrounds.DiscussionThe integration of advanced image preprocessing techniques, the dynamic sparse BSAM structure, and the BiFPN has proven effective in improving detection accuracy and has the potential to transform the approach to monitoring and maintaining power transmission infrastructure.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"78 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501151","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}

{"title":"Intuitive and versatile bionic legs: a perspective on volitional control","authors":"Matthias Voß, Anne D. Koelewijn, Philipp Beckerle","doi":"10.3389/fnbot.2024.1410760","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1410760","url":null,"abstract":"Active lower limb prostheses show large potential to offer energetic, balance, and versatility improvements to users when compared to passive and semi-active devices. Still, their control remains a major development challenge, with many different approaches existing. This perspective aims at illustrating a future leg prosthesis control approach to improve the everyday life of prosthesis users, while providing a research road map for getting there. Reviewing research on the needs and challenges faced by prosthesis users, we argue for the development of versatile control architectures for lower limb prosthetic devices that grant the wearer full volitional control at all times. To this end, existing control approaches for active lower limb prostheses are divided based on their consideration of volitional user input. The presented methods are discussed in regard to their suitability for universal everyday control involving user volition. Novel combinations of established methods are proposed. This involves the combination of feed-forward motor control signals with simulated feedback loops in prosthesis control, as well as online optimization techniques to individualize the system parameters. To provide more context, developments related to volitional control design are touched on.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"336 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501152","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}

{"title":"Frontiers | Enhanced LSTM-based robotic agent for load forecasting in low-voltage distributed photovoltaic power distribution network","authors":"Xudong Zhang, Junlong Wang, Jun Wang, Hao Wang, Lijun Lu","doi":"10.3389/fnbot.2024.1431643","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1431643","url":null,"abstract":"To ensure the safe operation and dispatching control of a low-voltage distributed photovoltaic (PV) power distribution network (PDN), the load forecasting problem of the PDN is studied in this study. Based on deep learning technology, this paper proposes a robot-assisted load forecasting method for low-voltage distributed photovoltaic power distribution networks using enhanced long short-term memory (LSTM). This method employs the frequency domain decomposition (FDD) to obtain boundary points and incorporates a dense layer following the LSTM layer to better extract data features. The LSTM is used to predict low-frequency and high-frequency components separately, enabling the model to precisely capture the voltage variation patterns across different frequency components, thereby achieving high-precision voltage prediction. By verifying the historical operation data set of a low-voltage distributed PV-PDN in Guangdong Province, experimental results demonstrate that the proposed “FDD+LSTM” model outperforms both recurrent neural network and support vector machine models in terms of prediction accuracy on both time scales of 1 h and 4 h. Precisely forecast the voltage in different seasons and time scales, which has a certain value in promoting the development of the PDN and related technology industry chain.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"55 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141587450","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}

{"title":"On designing a configurable UAV autopilot for unmanned quadrotors","authors":"Ali Bhar, Mounir Sayadi","doi":"10.3389/fnbot.2024.1363366","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1363366","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) and quadrotors are being used in an increasing number of applications. The detection and management of forest fires is continually improved by the incorporation of new economical technologies in order to prevent ecological degradation and disasters. Using an inner-outer loop design, this paper discusses an attitude and altitude controller for a quadrotor. As a highly nonlinear system, quadrotor dynamics can be simplified by assuming several assumptions. Quadrotor autopilot is developed using nonlinear feedback linearization technique, LQR, SMC, PD, and PID controllers. Often, these approaches are used to improve control and to reject disturbances. PD-PID controllers are also deployed in the tracking and surveillance of smoke or fire by intelligent algorithms. In this paper, the efficiency using a combined PD-PID controllers with adjustable parameters have been studied. The performance was assessed by simulation using matlab Simulink. The computational study conducted to assess the proposed approach showed that the PD-PID combination presented in this paper yields promising outcomes.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"60 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191804","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}

{"title":"Editorial: Recent advances in image fusion and quality improvement for cyber-physical systems, volume II.","authors":"Xin Jin, Shin-Jye Lee, Michal Wozniak, Qian Jiang","doi":"10.3389/fnbot.2024.1422982","DOIUrl":"10.3389/fnbot.2024.1422982","url":null,"abstract":"","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"18 ","pages":"1422982"},"PeriodicalIF":3.1,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11167091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141310513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of robotic path planning and navigation point configuration based on convolutional neural networks","authors":"Jian Wu, Huan Li, Bangjie Li, Xiaolong Zheng, Daqiao Zhang","doi":"10.3389/fnbot.2024.1406658","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1406658","url":null,"abstract":"This study introduces a novel approach for enhancing robotic path planning and navigation by optimizing point configuration through convolutional neural networks (CNNs). Faced with the challenge of precise area coverage and the inefficiency of traditional traversal and intelligent algorithms (e.g., genetic algorithms, particle swarm optimization) in point layout, we proposed a CNN-based optimization model. This model not only tackles the issues of speed and accuracy in point configuration with Gaussian distribution characteristics but also significantly improves the robot's capability to efficiently navigate and cover designated areas with high precision. Our methodology begins with defining a coverage index, followed by an optimization model that integrates polygon image features with the variability of Gaussian distribution. The proposed CNN model is trained with datasets generated from systematic point configurations, which then predicts optimal layouts for enhanced navigation. Our method achieves an experimental result error of <8% on the test dataset. The results validate effectiveness of the proposed model in achieving efficient and accurate path planning for robotic systems.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"26 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141256689","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}

{"title":"A framework for neurosymbolic robot action planning using large language models","authors":"Alessio Capitanelli, Fulvio Mastrogiovanni","doi":"10.3389/fnbot.2024.1342786","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1342786","url":null,"abstract":"Symbolic task planning is a widely used approach to enforce robot autonomy due to its ease of understanding and deployment in engineered robot architectures. However, techniques for symbolic task planning are difficult to scale in real-world, highly dynamic, human-robot collaboration scenarios because of the poor performance in planning domains where action effects may not be immediate, or when frequent re-planning is needed due to changed circumstances in the robot workspace. The validity of plans in the long term, plan length, and planning time could hinder the robot's efficiency and negatively affect the overall human-robot interaction's fluency. We present a framework, which we refer to as Teriyaki, specifically aimed at bridging the gap between symbolic task planning and machine learning approaches. The rationale is training Large Language Models (LLMs), namely GPT-3, into a neurosymbolic task planner compatible with the Planning Domain Definition Language (PDDL), and then leveraging its generative capabilities to overcome a number of limitations inherent to symbolic task planners. Potential benefits include (i) a better scalability in so far as the planning domain complexity increases, since LLMs' response time linearly scales with the combined length of the input and the output, instead of super-linearly as in the case of symbolic task planners, and (ii) the ability to synthesize a plan action-by-action instead of end-to-end, and to make each action available for execution as soon as it is generated instead of waiting for the whole plan to be available, which in turn enables concurrent planning and execution. In the past year, significant efforts have been devoted by the research community to evaluate the overall cognitive capabilities of LLMs, with alternate successes. Instead, with Teriyaki we aim to providing an overall planning performance comparable to traditional planners in specific planning domains, while leveraging LLMs capabilities in other metrics, specifically those related to their short- and mid-term generative capabilities, which are used to build a look-ahead predictive planning model. Preliminary results in selected domains show that our method can: (i) solve 95.5% of problems in a test data set of 1,000 samples; (ii) produce plans up to 13.5% shorter than a traditional symbolic planner; (iii) reduce average overall waiting times for a plan availability by up to 61.4%.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"119 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141259394","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}

{"title":"Fine-grained image classification method based on hybrid attention module","authors":"Weixiang Lu, Ying Yang, Lei Yang","doi":"10.3389/fnbot.2024.1391791","DOIUrl":"https://doi.org/10.3389/fnbot.2024.1391791","url":null,"abstract":"To efficiently capture feature information in tasks of fine-grained image classification, this study introduces a new network model for fine-grained image classification, which utilizes a hybrid attention approach. The model is built upon a hybrid attention module (MA), and with the assistance of the attention erasure module (EA), it can adaptively enhance the prominent areas in the image and capture more detailed image information. Specifically, for tasks involving fine-grained image classification, this study designs an attention module capable of applying the attention mechanism to both the channel and spatial dimensions. This highlights the important regions and key feature channels in the image, allowing for the extraction of distinct local features. Furthermore, this study presents an attention erasure module (EA) that can remove significant areas in the image based on the features identified; thus, shifting focus to additional feature details within the image and improving the diversity and completeness of the features. Moreover, this study enhances the pooling layer of ResNet50 to augment the perceptual region and the capability to extract features from the network’s less deep layers. For the objective of fine-grained image classification, this study extracts a variety of features and merges them effectively to create the final feature representation. To assess the effectiveness of the proposed model, experiments were conducted on three publicly available fine-grained image classification datasets: Stanford Cars, FGVC-Aircraft, and CUB-200–2011. The method achieved classification accuracies of 92.8, 94.0, and 88.2% on these datasets, respectively. In comparison with existing approaches, the efficiency of this method has significantly improved, demonstrating higher accuracy and robustness.","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"89 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140841370","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}