期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到11,655篇文章
< 1 2 … 250 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
An improved Alpha-shape algorithm for extracting section contours of the super-high steel bridge tower using point clouds 认领 引用 被引量：2
1
作者 ZHANG Yiming ZHAO Tianhao +2 位作者 LIAO Ruixuan LI Haoqing WANG Hao 《Journal of Southeast University(English Edition)》 EI CAS 2026年第1期26-35,共10页
The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,a... The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,and temperature fluctuations can compromise the accuracy of contour extraction.To address these limitations,an improved Alpha-shape-based point cloud contour extraction method is proposed.The proposed approach uses a hierarchical strategy to process three-dimensional laser scanning point clouds.The processed data are then subjected to curvatureadaptive voxel filtering to reduce acquisition noise.In addition,an enhanced iterative closest point(ICP)variant with correspondence validation accurately aligns the discrete point cloud segments.The proposed curvature-responsive Alpha-shape framework enables multiscale contour delineation through topology-adaptive threshold modulation,which resolves boundary ambiguities in geometrically complex cross-sections.The method was experimentally validated using field-acquired measurement datasets from the Zhangjinggao Yangtze River Bridge tower segments,confirming its capability to reconstruct noncanonical cross-sectional geometries.Three contour extraction methods,including Poisson reconstruction,the conventional Alpha-shape algorithm,and random sample consensus with ICP(RANSAC-ICP),were compared to evaluate the performance of the proposed Alpha-shape algorithm.The results demonstrate that the proposed method achieves superior contour extraction accuracy and data reduction efficiency,highlighting its effectiveness in contour extraction tasks. 展开更多
关键词 super-high steel bridge tower point cloud contour extraction improved Alpha-shape algorithm
暂未订购 下载PDF
RRT*-GSQ:A hybrid sampling path planning algorithm for complex orchard scenarios 认领 引用
2
作者 ZHU Qingzhen ZHAO Jiamuyang +1 位作者 DAI Xu YU Yang 《农业工程学报》 EI CAS CSCD 北大核心 2026年第3期13-25,共13页
Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT*),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narr... Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT*),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT*-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT*,the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT*.Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications. 展开更多
关键词 robot path planning orchard improved RRT*algorithm Gaussian sampling autonomous navigation
暂未订购 下载PDF
Techno-economic co-optimization of CO2enhanced oil recovery strategies in a tight oil reservoir using coupled improved evolutionary algorithm and machine learning framework 认领 引用
3
作者 Shu-Qin Wen Bing Wei +2 位作者 Jun-Yu You Nan-Jiang Leng William Ampomah 《Petroleum Science》 SCIE EI CAS CSCD 2026年第5期2639-2654,共16页
Massive carbon dioxide(CO2)emissions drive climate change.Injecting CO2into unconventional reservoirsachieves both enhanced oil recovery(EOR)and geological sequestration.However,simultaneously optimizing oil exc... Massive carbon dioxide(CO2)emissions drive climate change.Injecting CO2into unconventional reservoirsachieves both enhanced oil recovery(EOR)and geological sequestration.However,simultaneously optimizing oil exchange ratio,CO2storage,and net present value remainschallenging.This study develops an integrated machine learning(ML)-based framework for multi-objective optimization of CO2-EOR.A high-resolution reservoir simulation was constructed from field data,and Latin hypercube sampling generateddiverse scenarios for proxy training.Mantel's test quantified correlations between input parameters and performance metrics,showing that injection strategy strongly controls net present value,whereas geological properties dominate CO2storage.Three ML models—random forest(RF),support vector regression,and artificial neural networks—were evaluated,with RF selected for its superior performance on small datasets.RF was embedded into an improved non-dominatedsorting genetic algorithm II,enhanced with grey difference degree,crowding distance,and adaptive differential evolution to improve diversity and efficiency.Finally,the technique for order preference by similarity to ideal solution ranked Pareto-optimal solutions through integrating oil productivity,storage,and economics.The proposed framework operationalizes simultaneoushigh-efficiency tight oil recovery and field-scale CO2geological storage,delivering quantitative design rules that embed low-carbon practice into upstream operations and advance the energy sector's greenerand sustainable transition. 展开更多
关键词 CO2enhanced oil recovery Multi-objective optimization Improved non-dominated sorting genetic algorithm II Unconventional oil reservoir
暂未订购 下载PDF
Pedestrian detection of infrared images based on an improved FCOS algorithm 认领 引用
4
作者 Fuzhen ZHU Hao HAN +1 位作者 Hengfei JIA Bing ZHU 《Optoelectronics Letters》 EI 2026年第2期105-110,共6页
The current infrared image pedestrian detectors have problems with high rates of false positives and false negatives. To solve these problems, we proposed an improved anchor-free fully convolutional one-stage object d... The current infrared image pedestrian detectors have problems with high rates of false positives and false negatives. To solve these problems, we proposed an improved anchor-free fully convolutional one-stage object detection(FCOS) algorithm. Firstly, we introduced the channel attention module squeeze excitation(SE)-Block in the FCOS backbone network, which was used to learn how to model the relative importance between different feature channels, and to achieve the weight recalibration of the features extracted from the convolution neural network, and improve the weight values that are more important for pedestrian target detection. Secondly, soft non-maximum suppression(Soft-NMS) replaced the conventional NMS within the algorithm's post-processing phase, which was used to reduce the probability of missed detection for occluded pedestrians. The experimental results show that our improved FCOS algorithm improves the average precision(AP) by 6.71% on the original dataset and 7.97% on the augmented KAIST pedestrian dataset compared with the original FCOS algorithm. Our improvements effectively meet the real-time requirements and there is no significant decrease in speed compared with the original FCOS algorithm, and decreased the false positives and false negatives for infrared image pedestrian detection. 展开更多
关键词 weight recalibration pedestrian detection infrared image pedestrian detectors channel attention module learn how model relative importance different feature channels improved FCOS algorithm infrared images Squeeze Excitation SE block
暂未订购 下载PDF
Fault Self-Healing Cooperative Strategy of New Energy Distribution Network Based on Improved Ant Colony-Genetic Hybrid Algorithm 认领 引用 被引量：1
5
作者 Fengchao Chen Aoqi Mei +2 位作者 Zheng Liu Ruhao Wu Qiwei Li 《Energy Engineering》 EI 2026年第4期247-267,共21页
With the high proportion of new energy access,the traditional fault self-healing mechanism of the distribution network is challenged.Aiming at the demand for fast recovery of new distribution network faults,this paper... With the high proportion of new energy access,the traditional fault self-healing mechanism of the distribution network is challenged.Aiming at the demand for fast recovery of new distribution network faults,this paper proposes a fault self-healing cooperative strategy for the new energy distribution network based on an improved ant colony-genetic hybrid algorithm.Firstly,the graph theory adjacency matrix is used to characterize the topology of the distribution network,and the dynamic positioning of new energy nodes is realized.Secondly,based on the output model and load characteristic model of wind,photovoltaic,and energy storage,a two-layer cooperative self-healing model of the distribution network is constructed.The upper layer is based on the improved depth-breadth hybrid search(DFS-BFS)to divide the island,with the maximum weight load recovery and the minimum number of switching actions as the goal,combined with the load priority to dynamically restore the key load.The lower layer uses the improved ant colony-genetic hybrid algorithm to solve the fault recovery path with the minimum total power loss load and the minimum network loss as the goal,generate the optimal switching sequence,and verify the power flow constraints.Finally,the simulation results based on the IEEE 33-bus system show that the proposed method can guarantee the power supply of key loads in the distribution network with high-tech energy penetration,restore the power supply of more load nodes with the least switching operation,and effectively reduce the line loss,which verifies the effectiveness and superiority of the method. 展开更多
关键词 Fault recovery identification of topology improved ant colony-genetic hybrid algorithm distribution network self-healing
暂未订购 下载PDF
An improved YOLOv8 apple leaf disease detection algorithm 认领 引用
6
作者 Xinyu PEI Wei YUAN +1 位作者 Yuexiu ZHANG Lianjun SONG 《Optoelectronics Letters》 EI 2026年第5期314-320,共7页
This paper suggests an improved you only look once version 8n(YOLOv8n)algorithm for apple leaf disease detection,abbreviated as ALWB-YOLOv8n.The model is comprised of four essential components.Initially,arbitrary kern... This paper suggests an improved you only look once version 8n(YOLOv8n)algorithm for apple leaf disease detection,abbreviated as ALWB-YOLOv8n.The model is comprised of four essential components.Initially,arbitrary kernel convolution(AKConv)replaces the convolution module,which significantly decreases both the model’s parameter count and its overall size.Secondly,the large selective kernel network(LSKNet)attention mechanism is added in the Backbone,which can dynamically adjust the spatial sensory domain,and experiments have proved that this method is extremely advantageous for small target detection.Third,a weighted bi-directional feature pyramid network is introduced,which enables the model to achieve multi-scale feature fusion and is more concise and faster.Finally,wise intersection over union(WIoU)is used to replace complete intersection over union(CIoU)in YOLOv8,and the idea of focal loss is introduced,which effectively solves the detection problems in cases such as apple leaves occluding each other and blurred boundaries of diseased leaves.The improved algorithm exhibits superior performance compared to other common object detection algorithms.Compared with YOLOv8n,the improved algorithm achieves 2.3%improvement in precision,3.8%improvement in recall,and 2.5%and 2.7%improvement in mAP0.5 and mAP0.5:0.95,respectively.Compared with YOLOv8n,the improved model reduces the number of parameters and size of the model and realizes real-time monitoring with a frames per second(FPS)of 50.5. 展开更多
关键词 large selective kernel network arbitrary kernel convolution large selective kernel network lsknet attention kernel convolution akconv replaces weighted bi directional feature pyramid network adjust spatial sensory domainand improved YOLOv n algorithm apple leaf disease detection
暂未订购 下载PDF
UAV trajectory planning based on improved bidirectional RRT algorithm 认领 引用 被引量：2
7
作者 WANG Mengqiao LIU Erlin 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2025年第4期578-587,共10页
In response to the problems of low sampling efficiency,strong randomness of sampling points,and the tortuous shape of the planned path in the traditional rapidly-exploring random tree(RRT)algorithm and bidirectional R... In response to the problems of low sampling efficiency,strong randomness of sampling points,and the tortuous shape of the planned path in the traditional rapidly-exploring random tree(RRT)algorithm and bidirectional RRT algorithm used for unmanned aerial vehicle(UAV)path planning in complex environments,an improved bidirectional RRT algorithm was proposed.The algorithm firstly adopted a goal-oriented strategy to guide the sampling points towards the target point,and then the artificial potential field acted on the random tree nodes to avoid collision with obstacles and reduced the length of the search path,and the random tree node growth also combined the UAV’s own flight constraints,and by combining the triangulation method to remove the redundant node strategy and the third-order B-spline curve for the smoothing of the trajectory,the planned path was better.The planned paths were more optimized.Finally,the simulation experiments in complex and dynamic environments showed that the algorithm effectively improved the speed of trajectory planning and shortened the length of the trajectory,and could generate a safe,smooth and fast trajectory in complex environments,which could be applied to online trajectory planning. 展开更多
关键词 complex environment bidirectional RRT algorithm target orientation strategy artificial potential field method triangular inequality cut cubic B-spline online trajectory planning
暂未订购 下载PDF
Deep Learning Mixed Hyper-Parameter Optimization Based on Improved Cuckoo Search Algorithm 认领 引用 被引量：1
8
作者 TONG Yu CHEN Rong HU Biling 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2025年第2期195-204,共10页
Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,... Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,integer,or mixed,and are often given based on experience but largely affect the effectiveness of activity recognition.In order to adapt to different hyper-parameter optimization problems,our improved Cuckoo Search(CS)algorithm is proposed to optimize the mixed hyper-parameters in deep learning algorithm.The algorithm optimizes the hyper-parameters in the deep learning model robustly,and intelligently selects the combination of integer type and continuous hyper-parameters that make the model optimal.Then,the mixed hyper-parameter in Convolutional Neural Network(CNN),Long-Short-Term Memory(LSTM)and CNN-LSTM are optimized based on the methodology on the smart home activity recognition datasets.Results show that the methodology can improve the performance of the deep learning model and whether we are experienced or not,we can get a better deep learning model using our method. 展开更多
关键词 improved Cuckoo Search algorithm mixed hyper-parameter optimization deep learning
暂未订购 下载PDF
Damage Detection of X-ray Image of Conveyor Belts with Steel Rope Cores Based on Improved FCOS Algorithm 认领 引用 被引量：1
9
作者 WANG Baomin DING Hewei +1 位作者 TENG Fei LIU Hongqin 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第2期309-318,共10页
Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is propose... Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is proposed based on the improved fully convolutional one-stage object detection(FCOS)algorithm.The regression performance of bounding boxes was optimized by introducing the complete intersection over union loss function into the improved algorithm.The feature fusion network structure is modified by adding adaptive fusion paths to the feature fusion network structure,which makes full use of the features of accurate localization and semantics of multi-scale feature fusion networks.Finally,the network structure was trained and validated by using the X-ray image dataset of damages in conveyor belts with steel rope cores provided by a flaw detection equipment manufacturer.In addition,the data enhancement methods such as rotating,mirroring,and scaling,were employed to enrich the image dataset so that the model is adequately trained.Experimental results showed that the improved FCOS algorithm promoted the precision rate and the recall rate by 20.9%and 14.8%respectively,compared with the original algorithm.Meanwhile,compared with Fast R-CNN,Faster R-CNN,SSD,and YOLOv3,the improved FCOS algorithm has obvious advantages;detection precision rate and recall rate of the modified network reached 95.8%and 97.0%respectively.Furthermore,it demonstrated a higher detection accuracy without affecting the speed.The results of this work have some reference significance for the automatic identification and detection of steel core conveyor belt damage. 展开更多
关键词 conveyer belts with steel rope cores damage X-ray image image detection improved fully convo-lutional one-stage object detection(FCOS)algorithm
暂未订购 下载PDF
Design of high phase-sensitivity BlueP/TMDC heterostructure-based SPR biosensor using improved artificial bee colony algorithm 认领 引用
10
作者 Chong Yue Mantong Chen +1 位作者 Yaopu Lang Qinggang Liu 《Nanotechnology and Precision Engineering》 EI CAS CSCD 2025年第2期113-122,共10页
This paper uses an innovative improved artificial bee colony(IABC)algorithm to aid in the fabrication of a highly responsive phasemodulation surface plasmon resonance(SPR)biosensor.In this biosensor’s sensing structu... This paper uses an innovative improved artificial bee colony(IABC)algorithm to aid in the fabrication of a highly responsive phasemodulation surface plasmon resonance(SPR)biosensor.In this biosensor’s sensing structure,a double-layer Ag-Au metal film is combined with a blue phosphoreneransition metal dichalcogenide(BlueP/TMDC)hybrid structure and graphene.In the optimization function of the IABC method,the reflectivity at resonance angle is incorporated as a constraint to achieve high phase sensitivity.The performance of the Ag-Au-BlueP/TMDC-graphene heterostructure as optimized by the IABC method is compared with that of a similar structure optimized using the traditional ABC algorithm.The results indicate that optimization using the IABC method gives significantly more phase sensitivity,together with lower reflectivity,than can be achieved with the traditional ABC method.The highest phase sensitivity of 3.662×106 °/RIU is achieved with a bilayer of BlueP/WS2 and three layers of graphene.Moreover,analysis of the electric field distribution demonstrates that the optimal arrangement can be utilized for enhanced detection of small biomolecules.Thus,given the exceptional sensitivity achieved,the proposed method based on the IABC algorithm has great promise for use in the design of high-performance SPR biosensors with a variety of multilayer structures. 展开更多
关键词 SPR Phase modulation Sensitivity Improved artificial bee colony algorithm BlueP/TMDC Heterostructure
暂未订购 下载PDF
Improved coati optimization algorithm through multi-strategy integration:from theoretical design to engineering applications 认领 引用
11
作者 Shuangxi LIU Ruizhe FENG +2 位作者 Yuxin WEI Wei HUANG Binbin YAN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2025年第12期1197-1210,共14页
Optimization problems are crucial for a wide range of engineering applications,as efficient solutions lead to better performance.This study introduces an improved coati optimization algorithm(ICOA)that overcomes the p... Optimization problems are crucial for a wide range of engineering applications,as efficient solutions lead to better performance.This study introduces an improved coati optimization algorithm(ICOA)that overcomes the primary limitations of the original coati optimization algorithm(COA),notably its insufficient population diversity and propensity to become trapped in local optima.To address these issues,the ICOA integrates three innovative strategies:Latin hypercube sampling(LHS),Lévyflight,and an adaptive local search.LHS is employed to ensure a diverse initial population,thereby laying a foundation for the optimization.Lévy-flight is utilized to facilitate an efficient global search,enhancing the algorithm’s ability to explore the solution space.The adaptive local search is designed to refine solutions,enabling more precise local exploration.Together,these strategies significantly improve the population’s quality and diversity,thereby improving the algorithm’s convergence accuracy and optimization capabilities.The performance of the ICOA is tested against several established algorithms,using 12 benchmark functions.Additionally,the ICOA’s practicality and effectiveness are demonstrated through application to a real-world engineering problem,specifically the design optimization of tension/compression springs.Simulation results show that the ICOA consistently outperforms the other algorithms,providing robust solutions for a wide range of optimization problems. 展开更多
关键词 Improved coati optimization algorithm(ICOA) Latin hypercube sampling(LHS) Lévy-flight Adaptive local search Multi-strategy Engineering applications
暂未订购 下载PDF
Research on three-dimensional attack area based on improved backtracking and ALPS-GP algorithms of air-to-air missile 认领 引用
12
作者 ZHANG Haodi WANG Yuhui HE Jiale 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第1期292-310,共19页
In the field of calculating the attack area of air-to-air missiles in modern air combat scenarios,the limitations of existing research,including real-time calculation,accuracy efficiency trade-off,and the absence of t... In the field of calculating the attack area of air-to-air missiles in modern air combat scenarios,the limitations of existing research,including real-time calculation,accuracy efficiency trade-off,and the absence of the three-dimensional attack area model,restrict their practical applications.To address these issues,an improved backtracking algorithm is proposed to improve calculation efficiency.A significant reduction in solution time and maintenance of accuracy in the three-dimensional attack area are achieved by using the proposed algorithm.Furthermore,the age-layered population structure genetic programming(ALPS-GP)algorithm is introduced to determine an analytical polynomial model of the three-dimensional attack area,considering real-time requirements.The accuracy of the polynomial model is enhanced through the coefficient correction using an improved gradient descent algorithm.The study reveals a remarkable combination of high accuracy and efficient real-time computation,with a mean error of 91.89 m using the analytical polynomial model of the three-dimensional attack area solved in just 10-4s,thus meeting the requirements of real-time combat scenarios. 展开更多
关键词 air combat three-dimensional attack area improved backtracking algorithm age-layered population structure genetic programming(ALPS-GP) gradient descent algorithm
暂未订购 下载PDF
基于改进RRT算法的采摘机械臂路径规划研究 认领 引用 被引量：2
13
作者 孙波 彭浩 +2 位作者 周健康 陈红明 赵伟 《农机化研究》 北大核心 2026年第3期169-177,共9页
为了使采摘机械臂在复杂环境下完成采摘任务,提出了改进RRT算法有效规划机器臂路径,以提高机械臂的避障能力。针对标准RRT算法在多自由度机械臂路径规划中存在规划耗时长、导向性较差,冗余节点多和路径质量差等问题,引入动态采样域策略... 为了使采摘机械臂在复杂环境下完成采摘任务,提出了改进RRT算法有效规划机器臂路径,以提高机械臂的避障能力。针对标准RRT算法在多自由度机械臂路径规划中存在规划耗时长、导向性较差,冗余节点多和路径质量差等问题,引入动态采样域策略和目标偏置概率策略,提高了算法的导向性和收敛速度。设置了机械臂路径规划的两种仿真实验环境,包含多个小球体的小型障碍物环境和一个大球体的大型障碍物环境,并进行仿真对比实验。在小型障碍物环境下的仿真结果表明,相比GB-RRT算法,改进算法的时间代价减少了87.79%、最终路径的节点数减少了95.08%、路径代价减少了14.63%;在大型障碍物环境下的仿真结果表明,GB-RRT算法路径规划失败,而改进算法能够规划出一条合理的路径,使机械臂顺利避开障碍物。 展开更多
关键词 采摘机械臂 路径规划 RRT避障算法 动态采样域策略 B样条曲线
暂未订购 下载PDF
基于人工势场法改进RRT*算法的无人船路径规划 认领 引用 被引量：1
14
作者 陈小龙 李明智 +3 位作者 汪雅琴 张橙橙 陈禹成 张赫轩 《舰船科学技术》 北大核心 2026年第5期151-157,共7页
针对无人船在障碍物环境中路径规划效率低、路径冗余大、平滑性差等问题,提出一种融合人工势场法的改进RRT*算法(Artificial Potential Field Rapidly-exploring Random Tree*,APF-RRT*)。该算法以人工势场模型为引导机制,构... 针对无人船在障碍物环境中路径规划效率低、路径冗余大、平滑性差等问题,提出一种融合人工势场法的改进RRT*算法(Artificial Potential Field Rapidly-exploring Random Tree*,APF-RRT*)。该算法以人工势场模型为引导机制,构建包含信息激励势场、引力势场和斥力势场的复合势能函数,从而有目的性地引导采样点朝向目标区域并远离障碍物,提高算法采样效率;同时引入动态步长策略加快扩展速度,最后结合贪心剪枝和路径平滑机制优化初始路径。在2种典型环境下进行仿真对比实验,结果表明文中改进RRT*算法在规划效率、路径长度、平滑度以及稳定性方面优于传统算法,平均规划时长、路径长度、初始路径节点数与改进前相比分别减少了50.88%、6.24%、30.29%。改进算法为无人船在障碍物环境下生成安全、高效的航行路径提供了有力保障。 展开更多
关键词 路径规划 RRT*算法 人工势场模型 复合势能函数 无人船
暂未订购 下载PDF
A dynamic fusion path planning algorithm for mobile robots incorporating improved IB-RRT∗and deep reinforcement learning 认领 引用 被引量：1
15
作者 刘安东 ZHANG Baixin +2 位作者 CUI Qi ZHANG Dan NI Hongjie 《High Technology Letters》 EI CAS 2023年第4期365-376,共12页
Dynamic path planning is crucial for mobile robots to navigate successfully in unstructured envi-ronments.To achieve globally optimal path and real-time dynamic obstacle avoidance during the movement,a dynamic path pl... Dynamic path planning is crucial for mobile robots to navigate successfully in unstructured envi-ronments.To achieve globally optimal path and real-time dynamic obstacle avoidance during the movement,a dynamic path planning algorithm incorporating improved IB-RRT∗and deep reinforce-ment learning(DRL)is proposed.Firstly,an improved IB-RRT∗algorithm is proposed for global path planning by combining double elliptic subset sampling and probabilistic central circle target bi-as.Then,to tackle the slow response to dynamic obstacles and inadequate obstacle avoidance of tra-ditional local path planning algorithms,deep reinforcement learning is utilized to predict the move-ment trend of dynamic obstacles,leading to a dynamic fusion path planning.Finally,the simulation and experiment results demonstrate that the proposed improved IB-RRT∗algorithm has higher con-vergence speed and search efficiency compared with traditional Bi-RRT∗,Informed-RRT∗,and IB-RRT∗algorithms.Furthermore,the proposed fusion algorithm can effectively perform real-time obsta-cle avoidance and navigation tasks for mobile robots in unstructured environments. 展开更多
关键词 mobile robot improved IB-RRT∗algorithm deep reinforcement learning(DRL) real-time dynamic obstacle avoidance
暂未订购 下载PDF
基于改进APF-IRRT*的机械臂路径规划研究 认领 引用 被引量：1
16
作者 戈海龙 董学富 +1 位作者 孙嘉宾 成巍 《制造技术与机床》 CAS 北大核心 2026年第6期75-82,共8页
针对传统Informed-RRT*(informing rapidly-exploring random trees with path distance lower bounds)算法在机械臂避障路径规划中目标导向性不足、随机性大、规划效率低以及路径冗余造成运动不平滑等问题,提出一种改进的APF-Inform... 针对传统Informed-RRT*(informing rapidly-exploring random trees with path distance lower bounds)算法在机械臂避障路径规划中目标导向性不足、随机性大、规划效率低以及路径冗余造成运动不平滑等问题,提出一种改进的APF-Informed RRT*算法。在采样阶段,引入人工势场法引导采样点,提高随机树扩展的目标导向性。在树生长阶段,通过碰撞检测缓存键和自适应变步长策略,缓存碰撞检测结果,并生成唯一标识为缓存键,通过与障碍物的距离动态调整步长,减少碰撞检测次数,提高搜索效率。在优化阶段,采用分段式贪心算法和三次B样条曲线优化路径。通过与Informed-RRT*、GoalBias-RRT*和RRT*算法在三维环境下的仿真对比实验。结果表明,相较于Informed-RRT*算法,APF-Informed RRT*算法路径长度减少14.08%,时间节约96.17%;与RRT*和GoalBias-RRT*算法相比,APF-Informed RRT*算法路径长度分别减少19.89%和12.24%,时间分别节约88.52%和12.5%,算法的搜索效率得到显著的提升。将算法应用到AUBO i5机械臂平台,机械臂能够成功避开障碍物并准确到达目标点,且各关节的运动曲线平滑,进一步证明了改进算法的有效性。 展开更多
关键词 机械臂 路径规划 Informed-RRT*算法 缓存键策略 分段贪心策略 三次B样条曲线
暂未订购 下载PDF
改进RRT-Connect与AFSA融合算法移动机器人路径规划 认领 引用 被引量：1
17
作者 陈志澜 古春祥 《山东大学学报（工学版）》 CAS CSCD 北大核心 2026年第3期73-83,共11页
针对RRT-Connect算法在路径规划中搜索效率低、目标导向性弱、路径冗余节点多、平滑性不佳的问题,在改进RRT-Connect算法与人工鱼群算法基础上,提出ARRT-Connect融合算法.该算法引入中间节点,采用目标偏置策略、引力势场引导、自适应步... 针对RRT-Connect算法在路径规划中搜索效率低、目标导向性弱、路径冗余节点多、平滑性不佳的问题,在改进RRT-Connect算法与人工鱼群算法基础上,提出ARRT-Connect融合算法.该算法引入中间节点,采用目标偏置策略、引力势场引导、自适应步长调节及剪枝优化,并结合B样条曲线平滑路径;改进人工鱼群算法步长与视野范围,增强全局搜索能力.试验表明,与RRT-Connect算法相比,ARRT-Connect融合算法在简单和复杂环境中平均耗时分别减少82.22%和76.92%,平均路径长度分别缩短17.41%和19.38%,平均节点数分别减少79.21%和77.84%.将其应用于现实场景,移动机器人路径长度和耗时明显缩短,路径转折更平缓,验证了该算法有效性、优越性与可行性. 展开更多
关键词 融合算法 移动机器人 路径规划 RRT-Connect 人工鱼群算法
暂未订购 下载PDF
多向人工势场法引导的RRT-Connect路径规划算法研究 认领 引用 被引量：1
18
作者 丁建军 梁甲杭 +3 位作者 胡志明 章超 叶子安 孙超 《机电工程》 CAS 北大核心 2026年第3期499-513,共15页
针对快速随机扩展树(RRT-Connect)算法的随机性强、搜索效率低、路径规划时间长的问题,提出了一种面向机械臂的多向人工势场法引导的RRT-Connect路径规划算法。首先,引入了多向随机树拓展策略,在初始节点与目标节点连线中点选取了第三... 针对快速随机扩展树(RRT-Connect)算法的随机性强、搜索效率低、路径规划时间长的问题,提出了一种面向机械臂的多向人工势场法引导的RRT-Connect路径规划算法。首先,引入了多向随机树拓展策略,在初始节点与目标节点连线中点选取了第三节点作为根节点,增加了随机树的连接概率;其次,在路径拓展过程中融入了虚拟人工势场法,构建了复合势场函数,该函数将环境信息转化为具有梯度特征的势能空间,其中,引力场结合路径平滑度约束与运动学模型生成了渐进优化的轨迹牵引力,引导随机树向目标节点拓展;斥力场梯度通过自适应参数动态调整,形成了柔性避障区域,实时感知障碍物,提高了算法的收敛速度与避障能力;最后,在二维平面与三维空间环境下进行了仿真分析,还进行了实物抓取实验,验证了该算法的性能。研究结果表明:相较于传统RRT-Connect算法,多向人工势场法引导的RRT-Connect算法的路径平均节点数减少了54.36%,平均路径长度降低了10.23%,路径规划运行时间缩短了53.12%;此外,将该算法结合视觉抓取网络GR-ConvNet,开展了路径规划与实际抓取试验,该算法的路径规划长度减少了15.97%,规划运行时间缩短了51.74%,平均迭代次数降低了27.63%。该算法显著提升了路径规划的效率与稳定性,可为机械臂实现高效自主路径规划提供有力支撑。 展开更多
关键词 机械臂 运动学建模 多向随机树 人工势场法 快速随机扩展树算法
暂未订购 下载PDF
Hybrid Flow Shop Rescheduling Approach Based on Hybrid-Driven Mechanism and Improved Multi-Objective WOA 认领 引用
19
作者 Feng Lv Xin Xu +1 位作者 Cheng Yang Yixuan Tang 《Computers, Materials & Continua》 SCIE EI 2026年第7期1982-2009,共28页
To ensure an effective disturbance response and maintain continuous production in hybrid flow shops,this paper focuses on the design of a rescheduling method.A rescheduling model is constructed that minimizes the make... To ensure an effective disturbance response and maintain continuous production in hybrid flow shops,this paper focuses on the design of a rescheduling method.A rescheduling model is constructed that minimizes the makespan,total tardiness,and scheme deviation degree.A hybrid rescheduling driving mechanism based on the latest completion time is designed to effectively trigger rescheduling.The Whale Optimization Algorithm(WOA)is improved by integrating the good point set theory,nonlinear control parameter strategy,and Differential Evolution(DE)algorithm.Moreover,non-dominated sorting and a dynamic external archive mechanism based on crowding distance are introduced to make it suitable for multi-objective optimization problems.The superiority of the Improved Multi-objective Whale Optimization Algorithm(IMOWOA)and the effectiveness of the improved mechanisms are verified through comparative experiments and ablation experiments.Taking the final assembly production line of an agricultural machinery equipment enterprise as an example,a rescheduling scheme is generated based on the practical production requirements,which verifies the feasibility and effectiveness of the proposed method. 展开更多
关键词 Hybrid flow shop production disturbance production rescheduling rescheduling driving mechanism improved multi-objective whale optimization algorithm
暂未订购 下载PDF
基于人工势场法改进的双向RRT路径规划算法 认领 引用
20
作者 郏泽萌 高焕兵 王雪秋 《电子科技》 2026年第3期47-56,共10页
针对RRT(Rapidly-exploring Random Tree)算法在机器人路径规划过程存在采样点随机性高、算法效率低、路径规划时间长以及规划路径冗长等问题,文中提出一种结合人工势场法的双向RRT路径规划算法。将传统RRT算法中单向扩展方式改为由起... 针对RRT(Rapidly-exploring Random Tree)算法在机器人路径规划过程存在采样点随机性高、算法效率低、路径规划时间长以及规划路径冗长等问题,文中提出一种结合人工势场法的双向RRT路径规划算法。将传统RRT算法中单向扩展方式改为由起点和终点同时进行扩展,在节点扩展时加入人工势场法进行引导,增加节点扩展的目的性。将固定步长改换为可变步长,使随机树可以更快地向目标点扩展。对生成路径进行剪枝处理,删除路径中的冗余节点,进一步缩短路径长度。利用MATLAB仿真平台在相同环境下对比所提改进算法与RRT-Connect算法、DRRT-Connect(Dynamic Rapidly-exploring Random Tree Connect)算法、GB(Goal-Biased)-RRT算法、A*算法、PRM(Probabilistic Road Map)算法的路径规划效果。仿真结果表明,所提改进算法与其他改进算法相比最短路径缩短了7%,最短搜索时间降低了65%,提高了算法的规划效率。将所提算法应用于机器人,结果证明了其具有较强可行性。 展开更多
关键词 路径规划 RRT算法 人工势场法 RRT-Connect算法 改进双向RRT算法 贪心算法 可变步长 剪枝优化处理
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 250 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈