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5,295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：
Dynamic Reconnaissance Task Planning for Multi-UAV Based on Learning-Enhanced Pigeon-Inspired Optimization 认领 引用
1
作者 Yalan Peng Haibin Duan 《Journal of Beijing Institute of Technology》 EI CAS 2026年第1期53-62,共10页
In dynamic and uncertain reconnaissance missions,effective task assignment and path planning for multiple unmanned aerial vehicles(UAVs)present significant challenges.A stochastic multi-UAV reconnaissance scheduling p... In dynamic and uncertain reconnaissance missions,effective task assignment and path planning for multiple unmanned aerial vehicles(UAVs)present significant challenges.A stochastic multi-UAV reconnaissance scheduling problem is formulated as a combinatorial optimization task with nonlinear objectives and coupled constraints.To solve the non-deterministic polynomial(NP)-hard problem efficiently,a novel learning-enhanced pigeon-inspired optimization(L-PIO)algorithm is proposed.The algorithm integrates a Q-learning mechanism to dynamically regulate control parameters,enabling adaptive exploration–exploitation trade-offs across different optimization phases.Additionally,geometric abstraction techniques are employed to approximate complex reconnaissance regions using maximum inscribed rectangles and spiral path models,allowing for precise cost modeling of UAV paths.The formal objective function is developed to minimize global flight distance and completion time while maximizing reconnaissance priority and task coverage.A series of simulation experiments are conducted under three scenarios:static task allocation,dynamic task emergence,and UAV failure recovery.Comparative analysis with several updated algorithms demonstrates that L-PIO exhibits superior robustness,adaptability,and computational efficiency.The results verify the algorithm's effectiveness in addressing dynamic reconnaissance task planning in real-time multi-UAV applications. 展开更多
关键词 unmanned aerial vehicle(UAV) pigeon-inspired optimization reinforcement learning dynamic task planning coverage path planning
暂未订购 下载PDF
Integrating wind field analysis in UAV path planning:Enhancing safety and energy efficiency for urban logistics 认领 引用 被引量：3
2
作者 Ruijia GU Yifei ZHAO Xinhui REN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第1期508-533,共26页
Shenzhen,a major city in southern China,has experienced rapid advancements in Unmanned Aerial Vehicle(UAV)technology,resulting in extensive logistics networks with thousands of daily flights.However,frequent disruptio... Shenzhen,a major city in southern China,has experienced rapid advancements in Unmanned Aerial Vehicle(UAV)technology,resulting in extensive logistics networks with thousands of daily flights.However,frequent disruptions due to its subtropical monsoon climate,including typhoons and gusty winds,present ongoing challenges.Despite the growing focus on operational costs and third-party risks,research on low-altitude urban wind fields remains scarce.This study addresses this gap by integrating wind field analysis into UAV path planning,introducing key innovations to the classical model.First,UAV wind resistance and turbulence constraints are analyzed,mapping high-wind-speed and turbulence-prone zones in the airspace.Second,wind dynamics are incorporated into path planning by considering airspeed and groundspeed variation,optimizing waypoint selection and flight speed adjustments to improve overall energy efficiency.Additionally,a wind-aware Theta*algorithm is proposed,leveraging wind vectors to expedite search process,while Computational Fluid Dynamics(CFD)techniques are employed to calculate wind fields.A case study of Shenzhen,examining wind patterns over the past decade,demonstrates a 6.23%improvement in groundspeed and a 7.69%reduction in energy consumption compared to wind-agnostic models.This framework advances UAV logistics by enhancing route safety and energy efficiency,contributing to more cost-effective operations. 展开更多
关键词 Drone logistics Energy consumption Hazardous field region Path planning Unmanned aerial vehicle(UAV) Urban wind fields
暂未订购 下载PDF
Regional Constraint Module-Based Multi-Agent Path Planning Approach for Car-like Agents 认领 引用 被引量：3
3
作者 FANG Chengyuan MAO Jianlin +2 位作者 LI Dayan WANG Ning WANG Niya 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期1003-1013,I0017,共11页
Multi-agent path finding(MAPF)is a challenging problem widely employed in automated docks and warehouse systems.However,when the above scenarios require car-like agents to perform the tasks,due to the complexity of th... Multi-agent path finding(MAPF)is a challenging problem widely employed in automated docks and warehouse systems.However,when the above scenarios require car-like agents to perform the tasks,due to the complexity of the environment and the specificity of the shape of the agents,numerous conflicts between agents may occur in the process of path planning,which seriously affects the efficiency of the system and leads to a long runtime.To address these above problems,we propose a regional constraint module-based car-like conflict-based search(RCM-CL-CBS),which sets up the safe region to detect the conflicts between agents,maximizing the selection of paths with larger spatial resources under the same cost,and specifies the safe-exclusive region for colliding agents,reducing the probability of agents'collisions within a certain region.We conduct experiments under four scenario types including factory and warehousing instances.Compared with the baseline algorithms,the experimental results denote that our method reduces the computational burden in terms of resolving agent conflicts,and improves the efficiency of problem-solving.In particular,in the warehouse scenario,compared to the car-like conflict-based search(CL-CBS),CL-CBS in the sequential framework(CL-CBS-SE),and improved CL-CBS(ICL-CBS),our method in the sequential framework minimizes the runtime by 89.6%,53.4%,and 46.6%,respectively. 展开更多
关键词 multi-agent system mobile robot path planning
暂未订购 下载PDF
2.5D process path planning for multi-genus shapes based on combining of topological and geometric properties of medial axis transformation 认领 引用 被引量：1
4
作者 Xin JIANG Wenjie ZHAO +3 位作者 Cheng SU Guanying HUO Shirong LI Zhiming ZHENG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第4期640-653,共14页
The 2.5D process is widely utilized in modern industries,with multi-genus cross-sections increasingly encountered in both additive and subtractive manufacturing.Tool paths for multigenus shapes often suffer from disco... The 2.5D process is widely utilized in modern industries,with multi-genus cross-sections increasingly encountered in both additive and subtractive manufacturing.Tool paths for multigenus shapes often suffer from discontinuities that lead to frequent tool liftings,and selfintersections in offset paths,adversely affecting machining accuracy and efficiency.In this context,path topology,stepover uniformity,and degeneration of offset paths represent three fundamental concerns that must be considered in an integrated manner in 2.5D path planning for multi-genus shapes.This study proposes a tool path planning method based on combining of topological and geometric characteristics of medial axis transformation for the shape with multi-genus.A region segmentation strategy tailored to multi-genus shapes is first introduced to prevent global selfintersections in equidistant offset paths.Subsequently,the graph structure of the segmented shape is extracted,and the minimization of tool liftings is formulated as a minimum path cover problem in an undirected graph.A Fermat-spiral-like path topology is adopted within sub-regions to preserve the connectivity of graph and ensure smooth transitions between successive layers of contourparallel paths.Numerical and physical experiment results confirm the proposed method's effectiveness in maintaining stepover uniformity,avoiding degeneration of global self-intersections,and ensuring path connectivity. 展开更多
关键词 2.5D process Medial axis transformation Multi-genus shape Topological segmentation Tool path planning
暂未订购 下载PDF
Multimodal Trajectory Generation for Robotic Motion Planning Using Transformer-Based Fusion and Adversarial Learning 认领 引用 被引量：1
5
作者 Shtwai Alsubai Ahmad Almadhor +3 位作者 Abdullah Al Hejaili Najib Ben Aoun Tahani Alsubait Vincent Karovic 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期848-869,共22页
In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we devel... In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation.The approach employs transformer models and adversarial training to map high-level intent to robotic motion.Information from multiple data sources,such as voice traits,hand and body keypoints,visual observations,and recorded paths,is integrated simultaneously.These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation.Based on this design,two different learning strategies are investigated.In the first step,grammar-constrained Linear Temporal Logic(LTL)expressions are created from multimodal human inputs.These expressions are subsequently decoded into robot trajectories.The second method generates trajectories directly from symbolic intent and linguistic data,bypassing an intermediate logical representation.Transformer encoders combine multiple types of information,and autoregressive transformer decoders generate motion sequences.Adding smoothness and speed limits during training increases the likelihood of physical feasibility.To improve the realism and stability of the generated trajectories during training,an adversarial discriminator is also included to guide them toward the distribution of actual robot motion.Tests on the NATSGLD dataset indicate that the complete system exhibits stable training behaviour and performance.In normalised coordinates,the logic-based pipeline has an Average Displacement Error(ADE)of 0.040 and a Final Displacement Error(FDE)of 0.036.The adversarial generator makes substantially more progress,reducing ADE to 0.021 and FDE to 0.018.Visual examination confirms that the generated trajectories closely align with observed motion patterns while preserving smooth temporal dynamics. 展开更多
关键词 Multimodal trajectory generation robotic motion planning transformer networks sensor fusion reinforcement learning generative adversarial networks
暂未订购 下载PDF
Path Planning for Unmanned Surface Vehicles in Dynamic Environments Based on Artificial Potential Field and Global Guided Reinforcement Learning 认领 引用 被引量：2
6
作者 Shanqiang Li Chaoxi Li 《哈尔滨工程大学学报（英文版）》 CSCD 2026年第2期575-586,共12页
For unmanned surface vehicles(USVs),how to find an effective,feasible path that substantially improves mission success rates and time efficiency in dynamic marine environments is a critical issue.To address the path p... For unmanned surface vehicles(USVs),how to find an effective,feasible path that substantially improves mission success rates and time efficiency in dynamic marine environments is a critical issue.To address the path planning problem for USVs using deep reinforcement learning(DRL)in dynamic ocean environments,an improved algorithm based on Deep Q-Networks(DQN)is proposed,which is called Fast Guided Deep Q-Network Algorithm(FG-DQN).This algorithm combines DQN with the artificial potential field(APF)method and uses the A*algorithm to initialize a guiding path in a global static environment and to provide prior knowledge for the USVs.Additionally,the configuration of the reward function using APF and the guiding path effectively reduces the frequency of random movements during the early exploration phase of the DQN algorithm,which accelerates convergence,improves the computational efficiency of path planning,and increases path safety.Finally,the performance of the presented algorithm is validated through experiments in a 2D environment.Compared with traditional reinforcement learning methods such as Q-learning and Sarsa,as well as the original DQN algorithm,FG-DQN is more effective for USV path planning. 展开更多
关键词 Deep reinforcement learning Path planning Unmanned surface vehicles Fast guided deep Q-Network algorithm
暂未订购 下载PDF
Dynamic Mechanisms of Land Use Conflict Under Main Function Oriented Zone Planning:A Case Study of Beijing-Tianjin-Hebei Region 认领 引用 被引量：1
7
作者 ZHENG Yang CHENG Linlin +2 位作者 WANG Junqi WANG Yifang CUI Huizhen 《Chinese Geographical Science》 SCIE CAS CSCD 2026年第2期320-336,共17页
Systematically analyzing the impact mechanisms of policy on Land Use Conflict(LUC)is crucial for constructing effective conflict mitigation strategies.However,previous research on how policy influences LUC remains rel... Systematically analyzing the impact mechanisms of policy on Land Use Conflict(LUC)is crucial for constructing effective conflict mitigation strategies.However,previous research on how policy influences LUC remains relatively limited.Focusing on the indirect driving role of policy on LUC,this study proposed County Development Level(CDL)under Major Function Oriented Zone Planning(MFOZP)guidance as an intermediary variable,bridging the implicit influence of MFOZP and the explicit changes in LUC.Using the Beijing-Tianjin-Hebei(BTH)region in China as a case study,we analyzed the spatio-temporal evolution characteristics of LUC and CDL for the periods 2000-2010 and 2010-2020,before and after MFOZP implementation.Panel models and Geographically Weighted Regression(GWR)were employed to explore the mechanism by which CDL influences LUC under MFOZP guidance.The results show that:1)MFOZP implementation effectively alleviates land use pressure from regional development,with LUC continuously declining at a rate of 2.41%,while CDL exhibits slight growth(3.84%),during 2010-2020.2)Under MFOZP guidance,CDL reduces pressure on Land Use Structure Conflict(LUSC)and Land Use Process Conflict(LUPC),enhances its inhibitory effect on Land Use Function Conflict(LUFC),and significantly contributes to LUC coordination,with notable spatial heterogeneity.3)The coupling relationship between CDL and LUC has improved post-implementation.Based on this,tailored LUC coordination strategies are proposed for different functional zones.This study confirms the effectiveness of MFOZP in coordinating LUC and provides a scientific reference for LUC research under policy frameworks and the governance of LUC in the BTH region. 展开更多
关键词 Land Use Conflict(LUC) Major Function Oriented Zone Planning(MFOZP) County Development Level(CDL) Beijing-Tianjin-Hebei(BTH)region,China
暂未订购 下载PDF
Dynamic Integration of Q-Learning and A-APF for Efficient Path Planning in Complex Underground Mining Environments 认领 引用 被引量：1
8
作者 Chang Su Liangliang Zhao Dongbing Xiang 《Computers, Materials & Continua》 SCIE EI 2026年第2期1017-1040,共24页
To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense,dynamic,unpredictable obstacles challenging conventional methods—this p... To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense,dynamic,unpredictable obstacles challenging conventional methods—this paper proposes a hybrid algorithm integrating Q-learning and improved A*-Artificial Potential Field(A-APF).Centered on theQ-learning framework,the algorithmleverages safety-oriented guidance generated byA-APF and employs a dynamic coordination mechanism that adaptively balances exploration and exploitation.The proposed system comprises four core modules:(1)an environment modeling module that constructs grid-based obstacle maps;(2)an A-APF module that combines heuristic search from A*algorithm with repulsive force strategies from APF to generate guidance;(3)a Q-learning module that learns optimal state-action values(Q-values)through spraying robot-environment interaction and a reward function emphasizing path optimality and safety;and(4)a dynamic optimization module that ensures adaptive cooperation between Q-learning and A-APF through exploration rate control and environment-aware constraints.Simulation results demonstrate that the proposed method significantly enhances path safety in complex underground mining environments.Quantitative results indicate that,compared to the traditional Q-learning algorithm,the proposed method shortens training time by 42.95% and achieves a reduction in training failures from 78 to just 3.Compared to the static fusion algorithm,it further reduces both training time(by 10.78%)and training failures(by 50%),thereby improving overall training efficiency. 展开更多
关键词 Q-learning A*algorithm artificial potential field path planning hybrid algorithm
暂未订购 下载PDF
A Fast Integrated Gait,Footstep,and Motion Planning Framework for Wheeled-legged Robots 认领 引用
9
作者 Renjie Li Wei Dong +5 位作者 Jiarui Sun Wenhao Li Hui Dong Yongzhuo Gao Qijun Wu Yi Long 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第2期607-621,共15页
Biological systems such as mountain goats and felines exhibit remarkable agility and adaptability when traversing complex terrains.Inspired by these capabilities,quadruped robots have been developed to mimic legged lo... Biological systems such as mountain goats and felines exhibit remarkable agility and adaptability when traversing complex terrains.Inspired by these capabilities,quadruped robots have been developed to mimic legged locomotion and improve mobility over uneven environments.To further enhance locomotion efficiency and terrain versatility,wheeled-legged robots integrate wheels and legs into a hybrid platform,enabling both high-speed traversal and robust ground contact in unstructured terrain.However,planning coordinated locomotion across diverse terrains remains challenging due to the nonlinear dynamics,complex terrain contact constraints,and multimodal locomotion capabilities.In this paper,we propose a real-time,integrated planning framework that jointly optimizes gait scheduling,footstep placement,and whole-body motion trajectories.Our method adopts a two-stage approach.First,a sampling-based planner generates candidate gait sequences and nominal footstep targets based on terrain features and kinematic feasibility.Second,a constrained trajectory optimizer reformulates the planning problem as a Quadratic Programming(QP)task to compute dynamically feasible base trajectories and corresponding ground reaction forces.This hybrid formulation balances planning efficiency and physical realism.The planned trajectories and contact forces are tracked using a hierarchical control architecture combining Model Predictive Control(MPC)and Whole-Body Control(WBC),enabling fast and stable execution on real hardware.Simulation and real-world experiments demonstrate that our approach enables adaptive gait transitions and improves terrain adaptability compared to traditional planners. 展开更多
关键词 Legged-wheeled robots Motion planning Gait planning Optimization
Analysis of Metaheuristic,Sampling-Based,Potential Field,and Predictive Control Methods for Path Planning in Simulated Underwater Settings 认领 引用
10
作者 Rubina Castro Bruno Silva +1 位作者 Luiz Guerreiro Lopes Fábio Mendonça 《Computers, Materials & Continua》 SCIE EI 2026年第8期498-523,共26页
Path planning for autonomous underwater vehicles requires reliable and computationally efficient methods,particularly in cluttered environments.This work presents a comparative evaluation of representative approaches,... Path planning for autonomous underwater vehicles requires reliable and computationally efficient methods,particularly in cluttered environments.This work presents a comparative evaluation of representative approaches,including metaheuristic optimization methods(continuous genetic algorithm,particle swarm optimization,gray wolf optimizer,and Jaya),a sampling-based method(probabilistic roadmap with genetic refinement),a reactive strategy(artificial potential fields),and a control-based approach(model predictive control with control barrier functions).The algorithms are assessed in a controlled two-dimensional simulated workspace with randomly generated obstacles and systematically increasing obstacle density.Each configuration is evaluated across multiple independent trials using metrics such as success rate,path length,and convergence behavior.The effect of environmental disturbances is examined by analyzing particle swarm optimization under Gauss–Markov current models.The results show that performance depends strongly on the ability to preserve feasibility as obstacle density increases.The probabilistic roadmap with genetic refinement demonstrated the highest robustness,maintaining feasibility across all scenarios,while particle swarm optimization provided a strong balance between path quality and reliability in low-to-moderate clutter.The introduction of current disturbances led to reduced efficiency and consistency.Statistical analysis confirmed significant differences among methods,highlighting that rank-based superiority does not necessarily reflect practical robustness in constrained environments. 展开更多
关键词 Autonomous underwater vehicles path planning collision avoidance metaheuristic optimization potential fields sampling-based planning predictive control
暂未订购 下载PDF
Planning and Control for Robot-Assisted Feeding System Towards the Disabled 认领 引用
11
作者 DAI Feifan PEI Zijun +1 位作者 WANG Pu CHEN Weidong 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第1期71-81,共11页
Eating is an essential activity for the disabled with upper limb impairments,and therefore numerous feeding robots are born.However,safety and reliability are two basic elements to build trust in assistive robots.Thus... Eating is an essential activity for the disabled with upper limb impairments,and therefore numerous feeding robots are born.However,safety and reliability are two basic elements to build trust in assistive robots.Thus,planning and control methods for a safe and reliable robot-assisted feeding system are developed.Firstly,the feeding task is expanded to include both pre-meal preparation and eating.The feeding task is then divided into five subtasks including door opening,bowl grasping and transferring,utensil fetching,food skewering,and food transferring.Meanwhile,the system is built from five levels,i.e.,user interface,task planning,motion planning,control,and perception.Secondly,the feeding task is decomposed into a series of motion primitives based on a motion-centric taxonomy.Then a set of states utilizing those primitives is constructed and then a finite state machine is employed as the task manager which can regulate the workflow during the feeding task.Thirdly,a safety-oriented motion planner,a food item selector,an admittance controller,and a collision detector are depicted.Finally,experiments in the laboratory and further in a rehabilitation hospital with stroke patients are conducted.The experimental results indicate that the system is safe and reliable. 展开更多
关键词 assistive robots robot-assisted feeding admittance control motion planning task planning
暂未订购 下载PDF
A Multi-Stage Expansion Planning Method for Rural Distribution Networks with Flexible Interconnection 认领 引用
12
作者 Yueyang Ji Yaohui Peng +4 位作者 Haoran Ji Xinran Na Yuxuan Chen Wei Li Shengbin Chen 《Energy Engineering》 EI 2026年第8期17-31,共15页
With the increasing penetration of distributed generations and continuous growth of loads,traditional rural distribution networks face severe challenges in both hosting capacity and reliability.Addressing these issues... With the increasing penetration of distributed generations and continuous growth of loads,traditional rural distribution networks face severe challenges in both hosting capacity and reliability.Addressing these issues requires planning approaches that strike a balance between economic efficiency in infrastructure development and resilience in operation.Considering the dynamic growth of distributed generations and rural loads over the planning horizon,this paper presents a multi-stage expansion planning approach that coordinates flexible interconnection devices(FIDs)with substation and line construction to improve both economic performance and system reliability.The proposed method account for the time-varying growth of DGs and loads,as well as the declining investment cost of power electronic devices across multiple planning stages.The model holistically considers both economic efficiency and operational reliability,formulating the problem as a mixed-integer second-order cone programming(MISOCP)model to ensure computational efficiency.Case studies conducted on a practical 138-node rural distribution network in Guangxi,China,demonstrate the effectiveness of the proposed method.Compared to traditional single-stage or singleresource planning strategies,results indicate that the proposed multi-stage coordinated strategy achieves a significant reduction in total annualized cost while simultaneously enhancing system reliability,effectively mitigating voltage violations,and achieving a 100%PV accommodation rate without curtailment.This work provides a practical and adaptive planning framework for rural distribution networks,offering valuable insights for achieving cost-effective and resilient network development under rural energy transition. 展开更多
关键词 Rural distribution networks coordinated planning flexible interconnection device multi-stage planning reliability
暂未订购 下载PDF
Optimization of Flying Ad Hoc Network Topology and Collaborative Path Planning for Multiple UAVs 认领 引用
13
作者 Ming He Peizhao Wang +2 位作者 Haihua Chen Bin Sun Hongpeng Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1339-1352,共14页
Multiple unmanned aerial vehicles(UAVs)play a vital role in monitoring and data collection in wide area environments with harsh conditions.In most scenarios,issues such as real-time data retrieval and real-time UAV po... Multiple unmanned aerial vehicles(UAVs)play a vital role in monitoring and data collection in wide area environments with harsh conditions.In most scenarios,issues such as real-time data retrieval and real-time UAV positioning are often disregarded,essentially neglecting the communication constraints.In this paper,we comprehensively address both the coverage of the target area and the data transmission capabilities of the flying ad hoc network(FANET).The data throughput of the network is therefore maximized by optimizing the network topology and UAV trajectories.The resultant optimization problem is effectively solved by the proposed reinforcement learning-based trajectory planning(RL-TP)algorithm and the convex-based topology optimization(C-TOP)algorithm sequentially.The RL-TP optimizes the UAV paths while considering the constraints of FANET.The C-TOP maximizes the data throughput of the network while simultaneously constraining the neighbors and transmit powers of the UAVs,which is shown to be a convex problem that can be efficiently solved in polynomial time.Simulations and field experimental results show that the proposed optimization strategy can effectively plan the UAV trajectories and significantly improve the data throughput of the FANET over the adaptive local minimum spanning tree(A-LMST)and cyclic pruning-assisted power optimization(CPAPO)methods. 展开更多
关键词 Convex optimization flying ad hoc network (FANET) path planning reinforcement learning (RL) topology control
暂未订购 下载PDF
Cooperative coverage path planning of multiple underwater gliders considering sonar detection performance and energy efficiency 认领 引用
14
作者 Hao Hu Tonghao Wang +1 位作者 Yang Cao Xingguang Peng 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第7期198-215,共18页
This paper proposes a Cooperative Coverage Path Planning method for Multiple Underwater Gliders(MUG-CCPP)that considers sonar detection performance and energy efficiency.Unlike traditional phased methods that require ... This paper proposes a Cooperative Coverage Path Planning method for Multiple Underwater Gliders(MUG-CCPP)that considers sonar detection performance and energy efficiency.Unlike traditional phased methods that require task allocation or area partitioning,our method directly optimizes collaborative paths to complete coverage tasks.We establish a regional detection range model for the sonar by combining environmental data(temperature,salinity,depth)with the Bellhop3D acoustic model and the sonar equation.To balance coverage rate and energy consumption,we design a two-stage fitness function.The first stage guarantees feasible solutions that satisfy coverage rate constraints while accounting for invalid and overlapping coverage,path intersections,energy consumption,and safety.The second stage minimizes energy consumption within the coverage area.Furthermore,we present a Discrete Search-Assisted(DSA)strategy to improve initial solution quality and coverage ordering.Simulation results show that our MUG-CCpP method outperforms a state-of-the-art phased method,achieving higher coverage rates and greater energy efficiency,thereby offering a practical solution for coverage detection tasks. 展开更多
关键词 Underwater glider Coverage path planning Sonar detection performance Fitness function Discrete search-assisted
暂未订购 下载PDF
Kinodynamic motion planning for legged mobile manipulator with large object's dynamics 认领 引用
15
作者 Kun Xu Qikai Li +4 位作者 Mingdi Dan Liangliang Han Yaobin Tian Jiawei Chen Xilun Ding 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第3期316-323,共8页
The extension of traditional manipulators'workspace through mobile platforms has broadened the spectrum of po-tential robotic applications.Legged mobile manipulators,in particular,have garnered increasing attentio... The extension of traditional manipulators'workspace through mobile platforms has broadened the spectrum of po-tential robotic applications.Legged mobile manipulators,in particular,have garnered increasing attention due to their capability to navigate diverse environments and tackle challenging terrains more effectively than the wheeled coun-terparts.This study investigates the intricacies of motion planning for legged mobile manipulators in the context of manipulating large objects.Legged manipulators can significantly enhance their ability to manipulate large objects through coordinated movements of their legs and bodies.However,the complexity of this task far surpasses that of ordinary mobile manipulation challenges.To address this complex problem,it is decomposed into two sub-problems,focusing on the dynamics of both the object and the robot.The direct collocation method is used to transform the original problem into nonlinear programming,and suitable initial values are obtained using whole-body inverse ki-nematics.Finally,we demonstrate robot's ability to perform tasks such as opening a heavy door and putting a tumbled chair upright,thereby illustrating the efficacy and practicality of our approach. 展开更多
关键词 Legged robot Mobile manipulation Trajectory optimization Motion planning
暂未订购 下载PDF
A novel flexible operation mode and mission planning method for shipborne helicopter groups 认领 引用
16
作者 Wei Han Xiaohua Han +4 位作者 Xinwei Wang Haonan Wu Fang Guo Jingyu Cong Xichao Su 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第4期207-232,共26页
To address efficient operation scheduling of shipboard helicopter groups under multi-mission demands and limited deck space,a novel Flexible Operation Mode(FOM)was proposed.Mission grouping,deck operation processes,an... To address efficient operation scheduling of shipboard helicopter groups under multi-mission demands and limited deck space,a novel Flexible Operation Mode(FOM)was proposed.Mission grouping,deck operation processes,and mission time were flexibilized to construct a mission planning method.From the perspective of the deck operation lifecycle,the scheduling problem was modeled as a six-stage mixed-integer program.A bi-level optimization framework was introduced,prioritizing maximization of mission time window satisfaction and secondarily minimizing mean deck operation time.Spatial evolution during the transportation phase was managed via an offline trajectory library that converted high-dimensional constraints into low-dimensional parameter mappings,significantly reducing real-time solution complexity.A Leader-Follower Particle Swarm Optimization(LFPSO)algorithm was developed,featuring a three-stage stochastic priority encoding and a mission-chain-driven launch-re-covery decoupling strategy to reduce decision coupling.A hierarchical population structure enhanced co-evolution of global search and local refinement.The case simulation results show that the proposed model and algorithm can effectively solve the deck operation scheduling problem in complex mission scenarios,and are significantly superior to the Continuous Operation Mode(COM)and the Fixed-process FOM(FFOM)in key performance indicators such as mission time window satisfaction,average deck operation time,and average mission flight time.Its effectiveness in enhancing system scheduling capability and performance stability has been verified.This research provides systematic support for the flexible construction and intelligent decision-making of ship aviation operation systems. 展开更多
关键词 Flexible operation mode Mission planning method Shipborne helicopter groups Leader-follower particle swarm optimization Deck operation scheduling
暂未订购 下载PDF
Dual-modal trajectory planning method for compound-wing UAV leveraging differential flatness in urban environments 认领 引用
17
作者 Teng Long Zhenlin Zhou +3 位作者 Jingliang Sun Junzhi Li Zihan Wang Dawei Liu 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第6期50-63,共14页
The rise of the low-altitude economy highlights the importance of compound-wing UAVs.However,achieving seamless and optimal trajectories across different flight modes remains a significant challenge due to inherent hi... The rise of the low-altitude economy highlights the importance of compound-wing UAVs.However,achieving seamless and optimal trajectories across different flight modes remains a significant challenge due to inherent high-order discontinuities during mode transitions.To address this limitation,the Spatial-Temporal Adaptive Dual-modal Trajectory Planning(STA-DTP)method for compound-wing UAVs is proposed.By leveraging the differential flatness characteristics of the compound-wing UAV's dual-modal dynamics,a Dynamic collocation-point-based Minimum Control Effort Polynomial(DMINCO)trajectory parameterization model is developed.Its polynomial design ensures high-order continuity and allows for decoupled spatial-temporal representation of dual-modal trajectories,significantly reducing optimization complexity.An adaptive collocation-point decision mechanism for dual-modal transition is designed to address the dependence of trajectory optimality on transition timing.Integrated with an Anytime framework,this mechanism facilitates the real-time generation of feasible dual-modal flight trajectories.Transition collocation points are adaptively assigned based on conflicts between trajectory states and dynamic constraints.Under available computational resources,trajectory optimality is progressively enhanced through the densification collocation-point strategy.Compared with typical trajectory planning algorithms(i.e.,STA-DTP,SFC-SCP,and GPOPS-II)using fixed transition-point strategies,simulation results demonstrate that the proposed method achieves improvements of 1–2 orders of magnitude in planning efficiency and reduces trajectory flight durations as well.Consequently,this work provides an efficient and optimal framework for trajectory planning of compound-wing UAVs in urban environments. 展开更多
关键词 Compound-wing UAV Dual-modal trajectory planning Differential flatness Anytime framework
暂未订购 下载PDF
Adaptive elite ant colony optimization for track planning in gravity-aided navigation with multi-feature fusion 认领 引用
18
作者 Jinbai Zhang Hao Zhou +4 位作者 Yun Xiao Xinshang Li Hong Li Yifeng Chen Zhicai Luo 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第5期112-126,共15页
Autonomous Underwater Vehicle track planning is critical for maritime defense missions,particularly in signal-denied and stealth-sensitive environments.Gravity-aided inertial navigation systems(GAINS),as a passive and... Autonomous Underwater Vehicle track planning is critical for maritime defense missions,particularly in signal-denied and stealth-sensitive environments.Gravity-aided inertial navigation systems(GAINS),as a passive and emission-free approach,offer strong potential for such missions.However,track planning under gravity constraints remains underexplored.This paper proposes an Adaptive Elite Ant Colony Optimization(AEACO)algorithm to address this gap.AEACO integrates two key strategies:an elite reinforcement mechanism inspired by genetic algorithms and a dynamic parameter adjustment method for pheromone-related variables.A gravity adaptability model is first established using fuzzy statistics and entropy-weighted feature fusion to identify navigable regions.AEACO then reinforces elite path segments and self-adjusts its parameters in response to gravity field variations.Experiments across 22real-world marine gravity scenarios show that AEACO consistently outperforms various classical methods.Specifically,it achieves up to 19%shorter paths,40%fewer turns,and 95%faster convergence.Unlike other Ant Colony Optimization(ACO)variants,AEACO operates without fixed parameters or external tuning,making it scalable and adaptable for real-time defense operations in complex underwater environments. 展开更多
关键词 Ant colony optimization Elite strategy Gravity-aided inertial navigation Track planning Gravity adaptive regions
暂未订购 下载PDF
An enhanced ecological network for spatial planning considering spatial conflicts and structural resilience 认领 引用
19
作者 Haowei Mu Shanchuan Guo +6 位作者 Xingang Zhang Bo Yuan Xiaoquan Pan Zilong Xia Xin Pan Shangwu Zhang Peijun Du 《Geography and Sustainability》 CSCD 2026年第2期116-128,共13页
The digital transformation of territorial spatial planning has underscored the urgent need to integrate ecological network into spatial planning practices.In response,we developed two innovative new tools,the Ecologic... The digital transformation of territorial spatial planning has underscored the urgent need to integrate ecological network into spatial planning practices.In response,we developed two innovative new tools,the Ecological Linkage Tool(ELT)and the Relative Spatial Conflict Index(RSCI),to enhance ecological networks applications by addressing spatial conflicts and structural resilience.The ELT identified ecological corridors within and outside irregular ecological sources,activation points,and stepping stones in parallel,and then constructed an intact ecological network.By integrating the RSCI and complex network metrics,the spatial conflicts and structural resilience were evaluated.The framework was implemented in the Hohhot-Baotou-Ordos-Yulin(HBOY)urban agglomeration,identifying a total of 5,814 corridors,of which 67%were classified as intra-patch and 33%as inter-patch.The number and distribution of these corridors were determined by the size and shape of the ecological sources,and the connectivity of intra-patch corridors was 34%higher than inter-patch corridors.According to the RSCI,60%of the corridors experienced spatial conflicts,with 21%involving production spaces or composite production-related conflicts.Moreover,Yulin served as a key hub in the ecological network,and Baotou had the highest network efficiency.Compound conflict corridors(involving production,living,and open spaces)had a greater impact on overall ecological network efficiency compared to those with single or dual conflicts.Meanwhile,the failure of 40%of corridors without spatial conflicts would directly result in a 96.9%decline in network efficiency,highlighting their critical role in maintaining network functionality.This study provides an enhanced ecological network application solution for the China Spatial Planning Observation Network(CSPON),supporting spatial planning practices. 展开更多
关键词 Ecological network Ecological corridor Spatial conflict Structural resilience Spatial planning
暂未订购 下载PDF
RRT*-GSQ:A hybrid sampling path planning algorithm for complex orchard scenarios 认领 引用
20
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
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