自主智能系统(英文)最新文献

{"title":"Efficient and accurate road crack detection technology based on YOLOv8-ES","authors":"Kaili Zeng,&nbsp;Rui Fan,&nbsp;Xiaoyu Tang","doi":"10.1007/s43684-025-00091-3","DOIUrl":"10.1007/s43684-025-00091-3","url":null,"abstract":"<div><p>Road damage detection is an important aspect of road maintenance. Traditional manual inspections are laborious and imprecise. With the rise of deep learning technology, pavement detection methods employing deep neural networks give an efficient and accurate solution. However, due to background diversity, limited resolution, and fracture similarity, it is tough to detect road cracks with high accuracy. In this study, we offer a unique, efficient and accurate road crack damage detection, namely YOLOv8-ES. We present a novel dynamic convolutional layer(EDCM) that successfully increases the feature extraction capabilities for small fractures. At the same time, we also present a new attention mechanism (SGAM). It can effectively retain crucial information and increase the network feature extraction capacity. The Wise-IoU technique contains a dynamic, non-monotonic focusing mechanism designed to return to the goal-bounding box more precisely, especially for low-quality samples. We validate our method on both RDD2022 and VOC2007 datasets. The experimental results suggest that YOLOv8-ES performs well. This unique approach provides great support for the development of intelligent road maintenance systems and is projected to achieve further advances in future applications.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00091-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143373272","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 cooperative jamming decision-making method based on multi-agent reinforcement learning","authors":"Bingchen Cai,&nbsp;Haoran Li,&nbsp;Naimin Zhang,&nbsp;Mingyu Cao,&nbsp;Han Yu","doi":"10.1007/s43684-025-00090-4","DOIUrl":"10.1007/s43684-025-00090-4","url":null,"abstract":"<div><p>Electromagnetic jamming is a critical countermeasure in defense interception scenarios. This paper addresses the complex electromagnetic game involving multiple active jammers and radar systems by proposing a multi-agent reinforcement learning-based cooperative jamming decision-making method (MA-CJD). The proposed approach achieves high-quality and efficient target allocation, jamming mode selection, and power control. Mathematical models for radar systems and active jamming are developed to represent a multi-jammer and multi-radar electromagnetic confrontation scenario. The cooperative jamming decision-making process is then modeled as a Markov game, where the QMix multi-agent reinforcement learning algorithm is innovatively applied to handle inter-jammer cooperation. To tackle the challenges of a parameterized action space, the MP-DQN network structure is adopted, forming the basis of the MA-CJD algorithm. Simulation experiments validate the effectiveness of the proposed MA-CJD algorithm. Results show that MA-CJD significantly reduces the time defense units are detected while minimizing jamming resource consumption. Compared with existing algorithms, MA-CJD achieves better solutions, demonstrating its superiority in cooperative jamming scenarios.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00090-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184710","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":"Enhanced bearing RUL prediction based on dynamic temporal attention and mixed MLP","authors":"Zhongtian Jin,&nbsp;Chong Chen,&nbsp;Aris Syntetos,&nbsp;Ying Liu","doi":"10.1007/s43684-024-00088-4","DOIUrl":"10.1007/s43684-024-00088-4","url":null,"abstract":"<div><p>Bearings are critical components in machinery, and accurately predicting their remaining useful life (RUL) is essential for effective predictive maintenance. Traditional RUL prediction methods often rely on manual feature extraction and expert knowledge, which face specific challenges such as handling non-stationary data and avoiding overfitting due to the inclusion of numerous irrelevant features. This paper presents an approach that leverages Continuous Wavelet Transform (CWT) for feature extraction, a Channel-Temporal Mixed MLP (CT-MLP) layer for capturing intricate dependencies, and a dynamic attention mechanism to adjust its focus based on the temporal importance of features within the time series. The dynamic attention mechanism integrates multi-head attention with innovative enhancements, making it particularly effective for datasets exhibiting non-stationary behaviour. An experimental study using the XJTU-SY rolling bearings dataset and the PRONOSTIA bearing dataset revealed that the proposed deep learning algorithm significantly outperforms other state-of-the-art algorithms in terms of RMSE and MAE, demonstrating its robustness and accuracy.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00088-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939427","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":"An interaction-fair semi-decentralized trajectory planner for connected and autonomous vehicles","authors":"Zhengqin Liu,&nbsp;Jinlong Lei,&nbsp;Peng Yi,&nbsp;Yiguang Hong","doi":"10.1007/s43684-024-00087-5","DOIUrl":"10.1007/s43684-024-00087-5","url":null,"abstract":"<div><p>Lately, there has been a lot of interest in game-theoretic approaches to the trajectory planning of autonomous vehicles (AVs). But most methods solve the game independently for each AV while lacking coordination mechanisms, and hence result in redundant computation and fail to converge to the same equilibrium, which presents challenges in computational efficiency and safety. Moreover, most studies rely on the strong assumption of knowing the intentions of all other AVs. This paper designs a novel autonomous vehicle trajectory planning approach to resolve the computational efficiency and safety problems in uncoordinated trajectory planning by exploiting vehicle-to-everything (V2X) technology. Firstly, the trajectory planning for connected and autonomous vehicles (CAVs) is formulated as a game with coupled safety constraints. We then define the interaction fairness of the planned trajectories and prove that interaction-fair trajectories correspond to the variational equilibrium (VE) of this game. Subsequently, we propose a semi-decentralized planner for the vehicles to seek VE-based fair trajectories, in which each CAV optimizes its individual trajectory based on neighboring CAVs’ information shared through V2X, and the roadside unit takes the role of updating multipliers for collision avoidance constraints. The approach can significantly improve computational efficiency through parallel computing among CAVs, and enhance the safety of planned trajectories by ensuring equilibrium concordance among CAVs. Finally, we conduct Monte Carlo experiments in multiple situations at an intersection, where the empirical results show the advantages of SVEP, including the fast computation speed, a small communication payload, high scalability, equilibrium concordance, and safety, making it a promising solution for trajectory planning in connected traffic scenarios. To the best of our knowledge, this is the first study to achieve semi-distributed solving of a game with coupled constraints in a CAV trajectory planning problem.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00087-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912803","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":"Network synchronizability enhancement via adding antagonistic interactions","authors":"Yue Song,&nbsp;Xiaoqin Liu,&nbsp;Dingmei Wang,&nbsp;Pengfei Gao,&nbsp;Mengqi Xue","doi":"10.1007/s43684-024-00086-6","DOIUrl":"10.1007/s43684-024-00086-6","url":null,"abstract":"<div><p>We discover a “less-is-more” effect that adding local antagonistic interactions (negative edge weights) can enhance the overall synchronizability of a dynamical network system. To explain this seemingly counterintuitive phenomenon, a condition is established to identify those edges the weight reduction of which improves the synchronizability index of the underlying network. We further reveal that this condition can be interpreted from the perspective of resistance distance and network community structure. The obtained result is also verified via numerical experiments on a 14-node network and a 118-node network. Our finding brings new thoughts and inspirations to the future directions of optimal network design problems.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00086-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906061","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":"Distributed strategies for pursuit-evasion of high-order integrators","authors":"Panpan Zhou,&nbsp;Yueyue Xu,&nbsp;Bo Wahlberg,&nbsp;Xiaoming Hu","doi":"10.1007/s43684-024-00085-7","DOIUrl":"10.1007/s43684-024-00085-7","url":null,"abstract":"<div><p>This paper presents decentralized solutions for pursuit-evasion problems involving high-order integrators with intracoalition cooperation and intercoalition confrontation. Distinct error variables and hyper-variables are introduced to ensure the control strategies to be independent of the relative velocities, accelerations and higher order information of neighbors. Consequently, our approach only requires agents to exchange position information or to measure the relative positions of the neighbors. The distributed strategies take into consideration the goals of intracoalition cooperation or intercoalition confrontation of the players. Furthermore, after establishing a sufficient and necessary condition for a class of high-order integrators, we present conditions for capture and formation control with exponential convergence for three scenarios: one-pursuer-one-evader, multiple-pursuer-one-evader, and multiple-pursuer-multiple-evader. It is shown that the conditions depend on the structure of the communication graph, the weights in the control law, and the expected formation configuration. Finally, the effectiveness of the proposed algorithm is demonstrated through simulation results.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00085-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142889926","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":"An intelligent surface roughness prediction method based on automatic feature extraction and adaptive data fusion","authors":"Xun Zhang,&nbsp;Sibao Wang,&nbsp;Fangrui Gao,&nbsp;Hao Wang,&nbsp;Haoyu Wu,&nbsp;Ying Liu","doi":"10.1007/s43684-024-00083-9","DOIUrl":"10.1007/s43684-024-00083-9","url":null,"abstract":"<div><p>Machining quality prediction based on cutting big data is the core focus of current developments in intelligent manufacturing. Presently, predictions of machining quality primarily rely on process and signal analyses. Process-based predictions are generally constrained to the development of rudimentary regression models. Signal-based predictions often require large amounts of data, multiple processing steps (such as noise reduction, principal component analysis, modulation, etc.), and have low prediction efficiency. In addition, the accuracy of the model depends on tedious manual parameter tuning. This paper proposes a convolutional neural network quality intelligent prediction model based on automatic feature extraction and adaptive data fusion (CNN-AFEADF). Firstly, by processing signals from multiple directions, time-frequency domain images with rich features can be obtained, which significantly benefit neural network learning. Secondly, the corresponding images in three directions are fused into one image by setting different fusion weight parameters. The optimal fusion weight parameters and window length are determined by the Particle Swarm Optimization algorithm (PSO). This data fusion method reduces training time by 16.74 times. Finally, the proposed method is verified by various experiments. This method can automatically identify sensitive data features through neural network fitting experiments and optimization, thereby eliminating the need for expert experience in determining the significance of data features. Based on this approach, the model achieves an average relative error of 2.95%, reducing the prediction error compared to traditional models. Furthermore, this method enhances the intelligent machining level.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00083-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142811308","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":"Safe motion planning and formation control of quadruped robots","authors":"Zongrui Ji,&nbsp;Yi Dong","doi":"10.1007/s43684-024-00084-8","DOIUrl":"10.1007/s43684-024-00084-8","url":null,"abstract":"<div><p>This paper introduces a motion planning and cooperative formation control approach for quadruped robots and multi-agent systems. First, in order to improve the efficiency and safety of quadruped robots navigating in complex environments, this paper proposes a new planning method that combines the dynamic model of quadruped robots and a gradient-optimized obstacle avoidance strategy without Euclidean Signed Distance Field. The framework is suitable for both static and slow dynamic obstacle environments, aiming to achieve multiple goals of obstacle avoidance, minimizing energy consumption, reducing impact, satisfying dynamic constraints, and ensuring trajectory smoothness. This approach differs in that it reduces energy consumption throughout the movement from a new perspective. Meanwhile, this method effectively reduces the impact of the ground on the robot, thus mitigating the damage to its structure. Second, we combine the dynamic control barrier function and the virtual leader-follower model to achieve efficient and safe formation control through model predictive control. Finally, the proposed algorithm is validated through both simulations and real-world scenarios testing.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00084-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142778519","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":"Point clouds to as-built two-node wireframe digital twin: a novel method to support autonomous robotic inspection","authors":"Farzad Azizi Zade,&nbsp;Arvin Ebrahimkhanlou","doi":"10.1007/s43684-024-00082-w","DOIUrl":"10.1007/s43684-024-00082-w","url":null,"abstract":"<div><p>Previous studies have primarily focused on converting point clouds (PC) into a dense mech of 3D finite element models, neglecting the conversion of PCs into as-built wireframe models with two-node elements for line elements such as beams and columns. This study aims to demonstrate the feasibility of this direct conversion, utilizing building framing patterns to create wireframe models. The study also integrates the OpenSeesPy package for modal analysis and double integration for bending estimation to demonstrate the application of the presented method in robotic inspection. Results indicate the successful conversion of a 4-story mass timber building PC to a 3D structural model with an average error of 7.5% under simplified assumptions. Further, two complex mass timber shed PCs were tested, resulting in detailed wireframe models. According to resource monitoring, our method can process ∼593 points/second, mostly affected by the number of neighbors used in the first stage of sparse points removal. Lastly, our method detects beams, columns, ceilings (floors), and walls with their directions. This research can facilitate various structural modeling directly based on PC data for digital twinning and autonomous robotic inspection.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00082-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737095","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":"Stabilization of nonlinear safety-critical systems by relaxed converse Lyapunov-barrier approach and its applications in robotic systems","authors":"Haoqi Li,&nbsp;Jiangping Hu,&nbsp;Xiaoming Hu,&nbsp;Bijoy K. Ghosh","doi":"10.1007/s43684-024-00081-x","DOIUrl":"10.1007/s43684-024-00081-x","url":null,"abstract":"<div><p>Combining safety objectives with stability objectives is crucial for safety-critical systems. Existing studies generally unified these two objectives by constructing Lyapunov-type barrier functions. However, insufficient analysis of key set relationships within the system may render the proposed safety and stability conditions conservative, and these studies also did not provide how to use such conditions to design safety-stability control strategies. This paper proposed a feasible and constructive design to achieve stabilization of safety-critical systems by a relaxed converse Lyapunov-barrier approach. By analyzing the relationships between a series of sets associated with the safety-critical system, the stability and safety conditions can be appropriately relaxed. Then, with the help of relaxed converse control Lyapunov-barrier functions (RCCLBFs), a theoretical result was obtained for the stability of affine nonlinear systems with safety constraints. Subsequently, a constructive method was developed for a second-order strict-feedback system to transform the process of solving RCCLBFs into a Lyapunov-like stabilization problem. Finally, the proposed safety-stability control method is exerted on a robotic system and demonstrated by simulations.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00081-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672387","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}