IET Cybersystems and Robotics最新文献

Visual Simultaneous Localization and Mapping for Highly Dynamic Environments 高度动态环境的视觉同步定位和映射

IF 1.5

IET Cybersystems and Robotics Pub Date : 2025-05-27 DOI: 10.1049/csy2.70014

Yuxin Zheng, Weichen Dai, Yu Zhang, Wenhao Guan, Chengfei Liu

引用次数: 0

Path Planning Method for Live Working Robot in the Power Industry 电力工业带电工作机器人的路径规划方法

IF 1.5

IET Cybersystems and Robotics Pub Date : 2025-05-15 DOI: 10.1049/csy2.70015

Haoning Zhao, Jiamin Guo, Chaoqun Wang, Rui Guo, Xuewen Rong, Lecheng Yang, Yuliang Zhao, Yibin Li

{"title":"Path Planning Method for Live Working Robot in the Power Industry","authors":"Haoning Zhao, Jiamin Guo, Chaoqun Wang, Rui Guo, Xuewen Rong, Lecheng Yang, Yuliang Zhao, Yibin Li","doi":"10.1049/csy2.70015","DOIUrl":"https://doi.org/10.1049/csy2.70015","url":null,"abstract":"Given the complexity of live working environments in power distribution networks, where autonomous obstacle avoidance by robots often involves numerous path nodes and low exploration efficiency, the Bidirectional Node-Controlled Rapidly Exploring Random Tree (BNC-RRT) algorithm is proposed. This algorithm guides path search by progressively altering the sampling area and employs a node control mechanism to constrain the random tree expansion and extract effective boundary points. This approach reduces the number of ineffective nodes and collision checks during the search process, thereby enhancing path planning efficiency. Comparative simulation experiments conducted in various scenarios demonstrate that this algorithm reduces the number of path nodes and improves planning efficiency compared to classical algorithms. Finally, real-world experiments on a live working robot developed by our team show that the proposed algorithm shortens the average path length by 8.6%, and reduces the average planning and movement times by 44.7% and 28.7%, respectively, compared to classical path planning algorithms. These results indicate that the algorithm effectively improves path planning efficiency and is suitable for live working tasks in the power distribution industry.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074479","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}

引用次数: 0

Multiagent Task Allocation for Dynamic Intelligent Space: Auction and Preemption With Ontology Knowledge Graph 动态智能空间的多智能体任务分配：基于本体知识图的拍卖与抢占

IF 1.5

IET Cybersystems and Robotics Pub Date : 2025-04-16 DOI: 10.1049/csy2.70013

Wei Li, Jianhang Shang, Guoliang Liu, Zhenhua Liu, Guohui Tian

{"title":"Multiagent Task Allocation for Dynamic Intelligent Space: Auction and Preemption With Ontology Knowledge Graph","authors":"Wei Li, Jianhang Shang, Guoliang Liu, Zhenhua Liu, Guohui Tian","doi":"10.1049/csy2.70013","DOIUrl":"https://doi.org/10.1049/csy2.70013","url":null,"abstract":"This paper introduces a pioneering dynamic system optimisation for multiagent (DySOMA) framework, revolutionising task scheduling in dynamic intelligent spaces with an emphasis on multirobot systems. The core of DySOMA is an advanced auction-based algorithm coupled with a novel task preemption ranking mechanism, seamlessly integrated with an ontology knowledge graph that dynamically updates. This integration not only enhances the efficiency of task allocation among robots but also significantly improves the adaptability of the system to environmental changes. Compared to other advanced algorithms, the DySOMA algorithm shows significant performance improvements, with its RLB 26.8% higher than that of the best-performing Consensus-Based Parallel Auction and Execution (CBPAE) algorithm at 10 robots and 29.7% higher at 20 robots, demonstrating its superior capability in balancing task loads and optimising task completion times in larger, more complex environments. DySOMA sets a new benchmark for intelligent robot task scheduling, promising significant advancements in the autonomy and flexibility of robotic systems in complex evolving environments.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836152","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}

引用次数: 0

Autonomous Navigation and Collision Avoidance for AGV in Dynamic Environments: An Enhanced Deep Reinforcement Learning Approach With Composite Rewards and Dynamic Update Mechanisms 动态环境下AGV自主导航与避碰：基于复合奖励和动态更新机制的增强深度强化学习方法

IF 1.5

IET Cybersystems and Robotics Pub Date : 2025-04-11 DOI: 10.1049/csy2.70012

Zijianglong Huang, Zhigang Ren, Tehuan Chen, Shengze Cai, Chao Xu

{"title":"Autonomous Navigation and Collision Avoidance for AGV in Dynamic Environments: An Enhanced Deep Reinforcement Learning Approach With Composite Rewards and Dynamic Update Mechanisms","authors":"Zijianglong Huang, Zhigang Ren, Tehuan Chen, Shengze Cai, Chao Xu","doi":"10.1049/csy2.70012","DOIUrl":"https://doi.org/10.1049/csy2.70012","url":null,"abstract":"With the booming development of logistics, manufacturing and warehousing fields, the autonomous navigation and intelligent obstacle avoidance technology of automated guided vehicles (AGVs) has become the focus of scientific research. In this paper, an enhanced deep reinforcement learning (DRL) framework is proposed, aiming to empower AGVs with the ability of autonomous navigation and obstacle avoidance in the unknown and variable complex environment. To address the problems of time-consuming training and limited generalisation ability of traditional DRL, we refine the twin delayed deep deterministic policy gradient algorithm by integrating adaptive noise attenuation and dynamic delayed updating, optimising both training efficiency and model robustness. In order to further strengthen the AGV's ability to perceive and respond to changes of a dynamic environment, we introduce a distance-based obstacle penalty term in the designed composite reward function, which ensures that the AGV is capable of predicting and avoiding obstacles effectively in dynamic scenarios. Experiments indicate that the AGV model trained by this algorithm presents excellent autonomous navigation capability in both static and dynamic environments, with a high task completion rate, stable and reliable operation, which fully proves the high efficiency and robustness of this method and its practical value.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822314","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}

引用次数: 0

Hybrid Attention Spike Transformer 混合型注意尖峰变压器

IF 1.5

IET Cybersystems and Robotics Pub Date : 2025-04-10 DOI: 10.1049/csy2.70010

Xiongfei Fan, Hong Zhang, Yu Zhang

引用次数: 0

RMF-ED: Real-Time Multimodal Fusion for Enhanced Target Detection in Low-Light Environments 基于实时多模态融合的低光环境下增强目标检测

IF 1.5

IET Cybersystems and Robotics Pub Date : 2025-04-07 DOI: 10.1049/csy2.70011

Yuhong Wu, Jinkai Cui, Kuoye Niu, Yanlong Lu, Lijun Cheng, Shengze Cai, Chao Xu

{"title":"RMF-ED: Real-Time Multimodal Fusion for Enhanced Target Detection in Low-Light Environments","authors":"Yuhong Wu, Jinkai Cui, Kuoye Niu, Yanlong Lu, Lijun Cheng, Shengze Cai, Chao Xu","doi":"10.1049/csy2.70011","DOIUrl":"https://doi.org/10.1049/csy2.70011","url":null,"abstract":"Accurate target detection in low-light environments is crucial for unmanned aerial vehicles (UAVs) and autonomous driving applications. In this study, the authors introduce a real-time multimodal fusion for enhanced detection (RMF-ED), a novel framework designed to overcome the limitations of low-light target detection. By leveraging the complementary capabilities of near-infrared (NIR) cameras and light detection and ranging (LiDAR) sensors, RMF-ED enhances detection performance. An advanced NIR generative adversarial network (NIR-GAN) model was developed to address the lack of annotated NIR datasets, integrating structural similarity index measure (SSIM) loss and L1 loss functions. This approach enables the generation of high-quality NIR images from RGB datasets, bridging a critical gap in training data. Furthermore, the multimodal fusion algorithm integrates RGB images, NIR images, and LiDAR point clouds, ensuring consistency and accuracy in proposal fusion. Experimental results on the KITTI dataset demonstrate that RMF-ED achieves performance comparable to or exceeding state-of-the-art fusion algorithms, with a computational time of only 21 ms. These features make RMF-ED an efficient and versatile solution for real-time applications in low-light environments.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143793286","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}

引用次数: 0

Flocking Navigation and Obstacle Avoidance for UAV Swarms Via Adaptive Risk Avoidance Willingness Mechanism 基于自适应风险规避意愿机制的无人机群群集导航与避障

IF 1.5

IET Cybersystems and Robotics Pub Date : 2025-04-03 DOI: 10.1049/csy2.70009

Chao Li, Xiaojia Xiang, Yihao Sun, Chao Yan, Yixin Huang, Tianjiang Hu, Han Zhou

{"title":"Flocking Navigation and Obstacle Avoidance for UAV Swarms Via Adaptive Risk Avoidance Willingness Mechanism","authors":"Chao Li, Xiaojia Xiang, Yihao Sun, Chao Yan, Yixin Huang, Tianjiang Hu, Han Zhou","doi":"10.1049/csy2.70009","DOIUrl":"https://doi.org/10.1049/csy2.70009","url":null,"abstract":"A swarm of unmanned aerial vehicles (UAVs) has been widely used in both military and civilian fields due to its advantages of high cost-effectiveness, high task efficiency and strong survivability. However, there are still challenges in flocking control of UAV swarms in complex environments with various obstacles. In this paper, we propose a flocking control and obstacle avoidance method for UAV swarms, which is called willingness control method (WCM). Specifically, we propose an adaptive risk avoidance willingness (ARAW) mechanism, in which each UAV has an ARAW coefficient representing its ARAW. As the distance from danger gets closer, the ARAW of the UAV to avoid danger increases. On this basis, an obstacle avoidance method for UAV swarms is designed, and an informed individual mechanism influenced by neighbour repulsion is introduced. By combining the hierarchical weighting Vicsek model (HWVEM), the UAV swarm system can simultaneously balance flocking navigation and obstacle avoidance tasks and adjust the priority of different tasks adaptively during the task process. Finally, under local communication constraints of the UAV, a series of simulation experiments as well as real-word experiments with up to 12 UAVs are conducted to verify the security and compactness of the proposed method.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143770495","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}

引用次数: 0

Adaptive Safe Braking and Distance Prediction for Overhead Cranes With Multivariation Using MLP 基于MLP的多变量桥式起重机自适应安全制动与距离预测

IF 1.5

IET Cybersystems and Robotics Pub Date : 2025-04-01 DOI: 10.1049/csy2.70007

Tenglong Zhang, Guoliang Liu, Huili Chen, Guohui Tian, Qingqiang Guo

{"title":"Adaptive Safe Braking and Distance Prediction for Overhead Cranes With Multivariation Using MLP","authors":"Tenglong Zhang, Guoliang Liu, Huili Chen, Guohui Tian, Qingqiang Guo","doi":"10.1049/csy2.70007","DOIUrl":"https://doi.org/10.1049/csy2.70007","url":null,"abstract":"The emergency braking and braking distance prediction of an overhead crane pose challenging issues in its safe operation. This paper employs a multilayer perceptron (MLP) to implement an adaptive safe distance prediction functionality for an overhead crane with multiple variations. First, a discrete model of an overhead crane is constructed, and a model predictive control (MPC) model with angle constraints is applied for safe braking. Second, we analysed and selected the input variations of the safe distance prediction model. Subsequently, we permuted the inputs to the MLP and analysed the effect of each input on the accuracy of the MLP in predicting safety distances separately. We constructed a training dataset, and a test dataset and we optimised the safe distance prediction model through the training dataset. Finally, we conducted a comparative analysis between the MLP and nlinfit algorithms, highlighting the superiority of MLP-based adaptive safety distance prediction for bridge cranes. Experiments confirm the method's ability to ensure minimal swing angle during the entire braking process to achieve safe braking. The results underscore the practical utility and novelty of the proposed algorithm.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749682","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}

引用次数: 0

Move to See More: Approaching Object With Partial Occlusion Using Large Multimodal Model and Active Object Detection 移动以查看更多：使用大型多模态模型和活动对象检测接近部分遮挡的对象

IF 1.5

IET Cybersystems and Robotics Pub Date : 2025-03-27 DOI: 10.1049/csy2.70008

Aoqi Wang, Guohui Tian, Yuhao Wang, Zhongyang Li

{"title":"Move to See More: Approaching Object With Partial Occlusion Using Large Multimodal Model and Active Object Detection","authors":"Aoqi Wang, Guohui Tian, Yuhao Wang, Zhongyang Li","doi":"10.1049/csy2.70008","DOIUrl":"https://doi.org/10.1049/csy2.70008","url":null,"abstract":"Active object detection (AOD) is a crucial task in the field of robotics. A key challenge in household environments for AOD is that the target object is often undetectable due to partial occlusion, which leads to the failure of traditional methods. To address the occlusion problem, this paper first proposes a novel occlusion handling method based on the large multimodal model (LMM). The method utilises an LMM to detect and analyse input RGB images and generates adjustment actions to progressively eliminate occlusion. After the occlusion is handled, an improved AOD method based on a deep Q-learning network (DQN) is used to complete the task. We introduce an attention mechanism to process image features, enabling the model to focus on critical regions of the input images. Additionally, a new reward function is proposed that comprehensively considers the bounding box of the target object and the robot's distance to the object, along with the actions performed by the robot. Experiments on the dataset and in real-world scenarios validate the effectiveness of the proposed method in performing AOD tasks under partial occlusion.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143707626","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}

引用次数: 0

Bioinspired framework for real-time collision detection with dynamic obstacles in cluttered outdoor environments using event cameras 生物启发的框架，实时碰撞检测与动态障碍物在混乱的室外环境中使用事件相机

IF 1.5

IET Cybersystems and Robotics Pub Date : 2025-03-14 DOI: 10.1049/csy2.70006

Meriem Ben Miled, Wenwen Liu, Yuanchang Liu

{"title":"Bioinspired framework for real-time collision detection with dynamic obstacles in cluttered outdoor environments using event cameras","authors":"Meriem Ben Miled, Wenwen Liu, Yuanchang Liu","doi":"10.1049/csy2.70006","DOIUrl":"https://doi.org/10.1049/csy2.70006","url":null,"abstract":"In the field of robotics and visual-based navigation, event cameras are gaining popularity due to their exceptional dynamic range, low power consumption, and rapid response capabilities. These neuromorphic devices facilitate the efficient detection and avoidance of fast moving obstacles, and address common limitations of traditional hardware. However, the majority of state-of-the-art event-based algorithms still rely on conventional computer vision strategies. The goal is to shift from the standard protocols for dynamic obstacle detection by taking inspiration from the time-computational paradigm of biological vision system. In this paper, the authors present an innovative framework inspired by a biological response mechanism triggered by approaching objects, enabling the perception and identification of potential collision threats. The method, validated through both simulation and real-world experimentation, charts a new path in the application of event cameras for dynamic obstacle detection and avoidance in autonomous unmanned aerial vehicles. When compared to conventional methods, the proposed approach demonstrates a success rate of 97% in detecting obstacles within real-world outdoor settings.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629855","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}

引用次数: 0