IET Cybersystems and Robotics最新文献

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148082382","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}

{"title":"Online Path Planning for Multi-Robot Multi-Source Seeking Using Distributed Gaussian Processes","authors":"Hua Huang,&nbsp;Hai Zhu,&nbsp;Xiaozhou Zhu,&nbsp;Wenjun Mei,&nbsp;Baosong Deng","doi":"10.1049/csy2.70030","DOIUrl":"10.1049/csy2.70030","url":null,"abstract":"<p>Multi-robot source seeking in unknown environments is challenging due to the difficulties in coordinating multi-robot sensing, information fusion and path planning. Existing approaches often struggle with computational scalability and search efficiency, particularly when dealing with multiple sources. In this paper, we develop a distributed multi-robot multi-source seeking strategy that enables robots to discover multiple sources using local sensing and neighbourhood communication. Our approach consists of three key components. First, we design a distributed mapping technique that leverages Gaussian processes for probabilistic inference across the entire environment and adapts it for a decentralised setup. Second, we formulate the source-seeking problem as an informative path planning problem and design a new information-theoretic objective function that combines predicted source locations with environmental uncertainty to prevent robots from being trapped at discovered sources. Third, we develop a tree search algorithm for planning the actions of robots over a fixed-horizon cycle. The algorithm generates a sequence of points leading to the most informative location. Based on the sequence, the robot is guided to the target location by taking a fixed-step movement inspired by the principles of model predictive control. Simulations validate our approach across different scenarios with varying numbers of sources and robots. In particular, the proposed information-theoretic heuristic outperforms the broadly used uncertainty-first and mean-gradient-first approaches, reducing search steps by up to 36.7%. Furthermore, our approach achieves an improvement of up to 63.8% in search efficiency compared to state-of-the-art coverage-based methods for multi-robot multi-source seeking problems. The average computational time of the proposed method is below 90 ms, supporting its feasibility for real-time applications.</p>","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145572076","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148094808","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}

{"title":"MASA-Net: Multi-Aspect Channel–Spatial Attention Network With Cross-Layer Feature Aggregation for Accurate Fungi Species Identification","authors":"Indranil Bera,&nbsp;Rajesh Mukherjee,&nbsp;Bidesh Chakraborty","doi":"10.1049/csy2.70029","DOIUrl":"10.1049/csy2.70029","url":null,"abstract":"<p>Accurate identification of fungal species is essential for effective diagnosis and treatment. Traditional microscopy-based methods are often subjective and time-consuming. Deep learning has emerged as a promising tool in this domain. However, existing deep learning models often struggle to generalise in the presence of class imbalance and subtle morphological differences, which are common in fungal image datasets. This study proposes MASA-Net, a deep learning framework that combines a fine-tuned DenseNet201 backbone with a multi-aspect channel–spatial attention (MASA) module. The attention mechanism refines spatial and channel-wise features by capturing multi-scale spatial patterns and adaptively emphasising informative channels. This enhances the network's ability to focus on diagnostically relevant fungal structures while suppressing irrelevant features. The MASA-Net is evaluated on the DeFungi dataset and demonstrates superior performance in terms of accuracy, precision, recall and <i>F</i>1-score. It also outperforms established attention mechanisms such as squeeze-and-excitation networks (SE) and convolutional block attention module (CBAM) under identical conditions. These results highlight MASA-Net's robustness and effectiveness in addressing class imbalance and structural variability, offering a reliable solution for automated fungal species identification.</p>","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146841","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 Visual Odometry Artificial Intelligence-Based Method for Trajectory Learning and Tracking Applied to Mobile Robots","authors":"Bernardo Manuel Pirozzo,&nbsp;Mariano De Paula,&nbsp;Sebastián Aldo Villar,&nbsp;Carola de Benito,&nbsp;Gerardo Gabriel Acosta,&nbsp;Rodrigo Picos","doi":"10.1049/csy2.70028","DOIUrl":"10.1049/csy2.70028","url":null,"abstract":"<p>Autonomous systems have demonstrated high performance in several applications. One of the most important is localisation systems, which are necessary for the safe navigation of autonomous cars or mobile robots. However, despite significant advances in this field, there are still areas open for research and improvement. Two of the most important challenges include the precise traversal of a bounded route and emergencies arising from the breakdown or failure of one or more sensors, which can lead to malfunction or system localisation failure. To address these issues, auxiliary assistance systems are necessary, enabling localisation for a safe return to the starting point, completing the trajectory, or facilitating an emergency stop in a designated area for such situations. Motivated by the exploration of applying artificial intelligence to pose estimation in a navigation system, this article introduces a monocular visual odometry method that, through teach and repeat, learns and autonomously replicates trajectories. Our proposal can serve as either a primary localisation system or an auxiliary assistance system. In the first case, our approach is applicable in scenarios where the traversing route remains unchanged. In the second case, the goal is to achieve a safe return to the starting point or to reach the end point of the trajectory. We initially utilised a publicly available dataset to showcase the learning capability and robustness under different visibility conditions to validate our proposal. Subsequently, we compared our approach with other well-known methods to assess performance metrics. Finally, we evaluated real-time trajectory replication on a ground robot, both simulated and real, across multiple trajectories of increasing complexity.</p>","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145038123","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148075118","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}

{"title":"Clustering Path Optimisation-Based 2-Opt Rapid Wax-Drawing Trajectory Planning for Industrial 3D Wax-Drawing Robots","authors":"Qiyuan Fu,&nbsp;Ping Liu,&nbsp;Qinglang Xie,&nbsp;Shidong Zhai,&nbsp;Mingjie Liu","doi":"10.1049/csy2.70025","DOIUrl":"10.1049/csy2.70025","url":null,"abstract":"<p>The tool path trajectory serves as a cornerstone of three-dimensional (3D) printing robot technology, and path optimisation algorithms are instrumental in enabling faster, more precise and higher-quality prints. This work proposes a clustering path optimisation-based 2-opt rapid wax-drawing trajectory planning method for 3D drawing robots. Firstly, the input wax-drawing image is preprocessed to extract contour information, which is then simplified into polygons. Next, the spiral and filling trajectory algorithms are used to convert the polygons into corresponding spiral and filling paths, which are modelled as nodes in the travelling salesman problem (TSP). An improved k-means++ clustering algorithm is then designed to adaptively divide the nodes into multiple clusters. Each cluster is subsequently planned using the improved ant colony optimisation (ACO) algorithm to find the shortest path. Afterwards, the nearest-neighbour algorithm is employed to connect the shortest paths of each cluster, forming an initial tool path. Finally, the 2-opt optimisation algorithm is incorporated to optimise the preliminary path, resulting in the optimal motion trajectory for the wax-drawing tool. The verification tests show that the proposed method achieves an average reduction in path length of 30.75% compared with the parallel scanning method, traditional ant colony optimisation, Christofides with 2-opt algorithm. Meanwhile, the 3D robot wax-drawing experiments demonstrate a 17.9% reduction in drawing time, significantly improving the efficiency of large-scale production and highlighting the practical value of 3D drawing robots.</p>","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145037520","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148072214","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}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148071429","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}

{"title":"RevFB-BEV: Memory-Efficient Network With Reversible Swin Transformer for 3D BEV Object Detection","authors":"Leilei Pan,&nbsp;Yingnan Guo,&nbsp;Yu Zhang","doi":"10.1049/csy2.70021","DOIUrl":"10.1049/csy2.70021","url":null,"abstract":"<p>The perception of Bird's Eye View (BEV) has become a widely adopted approach in 3D object detection due to its spatial and dimensional consistency. However, the increasing complexity of neural network architectures has resulted in higher training memory, thereby limiting the scalability of model training. To address these challenges, we propose a novel model, RevFB-BEV, which is based on the Reversible Swin Transformer (RevSwin) with Forward-Backward View Transformation (FBVT) and LiDAR Guided Back Projection (LGBP). This approach includes the RevSwin backbone network, which employs a reversible architecture to minimise training memory by recomputing intermediate parameters. Moreover, we introduce the FBVT module that refines BEV features extracted from forward projection, yielding denser and more precise camera BEV representations. The LGBP module further utilises LiDAR BEV guidance for back projection to achieve more accurate camera BEV features. Extensive experiments on the nuScenes dataset demonstrate notable performance improvements, with our model achieving over a <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>4</mn>\u0000 <mo>×</mo>\u0000 </mrow>\u0000 <annotation> $4times $</annotation>\u0000 </semantics></math> reduction in training memory and a more than <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>12</mn>\u0000 <mo>×</mo>\u0000 </mrow>\u0000 <annotation> $12times $</annotation>\u0000 </semantics></math> decrease in single-backbone training memory. These efficiency gains become even more pronounced with deeper network architectures. Additionally, RevFB-BEV achieves 68.1 mAP (mean Average Precision) on the validation set and 68.9 mAP on the test set, which is nearly on par with the baseline BEVFusion, underscoring its effectiveness in resource-constrained scenarios.</p>","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145012633","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}