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123篇文章
< 1 2 … 7 >
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：
A Bio-inspired Bubble Artificial Muscles and TacTip Perception-driven Tri-legged Robot for Obstacle Avoidance 认领 引用
1
作者 Chaoqun Xiang Zhengwei Zhong +3 位作者 Wenqiang Wu Xiaocong Chen Yisheng Guan Tao Zou 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第1期175-191,共17页
Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary... Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary Soft Tri-Legged Robot(STLR)that improves movement and obstacle-avoidance skills by using a bio-inspired pneumatic artificial muscle(Bubble Artificial Muscles)and a bio-inspired tactile sensor(TacTip).The STLR is activated by BAMs,which are flexible,pneu-matic-driven actuators that provide fine control over forward,backward,and steering movements.Obstacle identification and avoidance are facilitated by the TacTip sensor,which delivers tactile input for traversing unstructured terrains.We delineate the mechanical features of the BAMs,assess the functionality of the robot's legs,and elaborate on the incorpora-tion of the tactile sensing system.Experimental results demonstrate that the STLR can effectively achieve multi-directional flexible movement and obstacle avoidance through a cross-modal perception-actuation mechanism.This study highlights the promise of soft robotics for search and rescue,medical aid,and autonomous exploration,while delineating difficulties and opportunities for future improvements in functionality and efficiency. 展开更多
关键词 Legged robot Bio-inspired bubble artificial muscles Bio-inspired TacTip sensor Foot tactile perception Obstacle avoidance
Design of a Patrol and Security Robot with Semantic Mapping and Obstacle Avoidance System Using RGB-D Camera and LiDAR 认领 引用
2
作者 Shu-Yin Chiang Shin-En Huang 《Computers, Materials & Continua》 SCIE EI 2026年第4期1735-1753,共19页
This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obsta... This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments. 展开更多
关键词 RGB-D semantic mapping object recognition obstacle avoidance security robot
暂未订购 下载PDF
DFCOA:Distributed Formation Control and Obstacle Avoidance for Multi-UGV Systems 认领 引用
3
作者 Md.Faishal Rahaman Xueyuan Li +3 位作者 Muhammad Amjad Ibrahim Gasimove Md.Shariful Islam S.M.Abul Bashar 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期922-954,共33页
Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential f... Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential for executing sophisticated collective tasks.This paper presents a Distributed Formation Control and Obstacle Avoidance(DFCOA)framework for multi-unmanned ground vehicles(UGV).DFCOA integrates a virtual leader structure for global guidance,an improved A*path planning algorithm with an advanced cost function for efficient path planning,and a repulsive-force-based improved vector field histogram star(VFH*)technique for collision avoidance.The virtual leader generates a reference trajectory while enabling distributed execution;the improved A*algorithm reduces planning time and number of nodes to determine the shortest path from the starting position to the goal;and the improved VFH*uses 2D LiDAR data with inter-agent repulsive force to simultaneously avoid collision with obstacles and maintain safe inter-vehicle distances.The formation stability of the proposed DFCOA reaches 95.8%and 94.6%in two scenarios,with root mean square(RMS)centroid errors of 0.9516 and 1.0008 m,respectively.Velocity tracking is precise(velocity centroid error RMS of 0.2699 and 0.1700 m/s),and linear velocities closely match the desired 0.3 m/s.Safety metrics showed average collision risks of 0.7773 and 0.5143,with minimum inter-vehicle distances of 0.4702 and 0.8763 m,confirming collision-free navigation of four UGVs.DFCOA outperforms conventional methods in formation stability,path efficiency,and scalability,proving its suitability for decentralized multi-UGV applications. 展开更多
关键词 Formation control obstacle avoidance virtual leader path planning multi UGV collaboration
暂未订购 下载PDF
Imitation Learning for Unmanned Aerial Vehicle Obstacle Avoidance Based on Visual Features with DAgger 认领 引用
4
作者 Yuqi Yang Mengyun Wang +1 位作者 Yifeng Niu Bo Wang 《Journal of Beijing Institute of Technology》 EI CAS 2026年第1期114-126,共13页
Unmanned aerial vehicles(UAVs)face the challenge of autonomous obstacle avoidance in complex,multi-obstacle environments.Behavior cloning offers a promising approach to rapidly acquire a learning policy from limited e... Unmanned aerial vehicles(UAVs)face the challenge of autonomous obstacle avoidance in complex,multi-obstacle environments.Behavior cloning offers a promising approach to rapidly acquire a learning policy from limited expert demonstrations.However,pure imitation learning inherently suffers from poor exploration and limited generalization,typically necessitating extensive datasets to train competent student policies.We utilize a cross-modal variational autoencoder(CM-VAE)to extract compact features from raw visual inputs and UAV states,which then feed into a policy network.We evaluated our approach in a simulated environment featuring a challenging circular trajectory with eight gate obstacles.The results demonstrate that the policy trained with pure behavior cloning consistently failed.In stark contrast,our DAgger-augmented behavior cloning method successfully traversed all gates without collision.Our findings confirm that DAgger effectively mitigates the shortcomings of behavior cloning,enabling the creation of reliable and sample-efficient navigation policies for UAVs. 展开更多
关键词 imitation learning unmanned aerial vehicle obstacle avoidance DAgger
暂未订购 下载PDF
Efficient obstacle avoidance planning for multi-robot suspension system based on a collaborative optimization for force and position 认领 引用 被引量：1
5
作者 Xiangtang Zhao Zhigang Zhao +2 位作者 Cheng Su Jiadong Meng Hutang Sang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2025年第12期135-154,共20页
To avoid collisions between a suspended object,cables,towing robots,and obstacles in the environment in a multi-robot suspension system,obstacle avoidance planning was studied based on a collaborative optimization met... To avoid collisions between a suspended object,cables,towing robots,and obstacles in the environment in a multi-robot suspension system,obstacle avoidance planning was studied based on a collaborative optimization method for force and position.Based on the analysis of the kinematics and dynamics of the system,the inverse kinematics and inverse dynamics of the system are solved using the least variance method.The obstacle avoidance planning is performed in the solved collisionfree feasible space using the stable dung beetle optimization(SDBO)algorithm,which ensures that the suspended object can move stably to the target point in the workspace.The optimal obstacle avoidance trajectory of the multi-robot suspension system can be accurately determined by using the collaborative optimization method for force and position to plan the towing robot and the cable.Finally,the correctness of the obstacle avoidance planning method is verified by simulations.By taking a special scenario,the remarkable findings reveal that the SDBO algorithm outperforms the dung beetle optimization algorithm by reducing the length of the planned trajectory of the suspended object by 14.51%and the height by 79.88%,and reducing the minimum fitness by 95.84%and the average fitness by 94.77%.The results can help the multi-robot suspension system to perform various towing tasks safely and stably,and extend the related planning and control theory. 展开更多
关键词 Suspension system Obstacle avoidance planning Collision-free feasible space Stable dung beetle optimization Collaborative optimization for force and position
暂未订购 下载PDF
Hazard-Aware Weighted Advantage Combination for UAV Target Tracking and Obstacle Avoidance 认领 引用
6
作者 Lele Xu Jian Liu +2 位作者 Xiaoguang Chang Xuping Liu Changyin Sun 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第6期1260-1271,共12页
In recent years,the rapid evolution of unmanned aerial vehicles(UAVs)has brought about transformative changes across various industries.However,addressing fundamental challenges in UAV technology,particularly target t... In recent years,the rapid evolution of unmanned aerial vehicles(UAVs)has brought about transformative changes across various industries.However,addressing fundamental challenges in UAV technology,particularly target tracking and obstacle avoidance,remains crucial for wildlife protection,military industry security,etc.Many existing methods based on reinforcement learning to solve UAV multi-tasks need to be redesigned and retrained,and cannot be quickly and effectively extended to other scenarios.To this end,we propose a novel solution based on a hazard-aware weighted advantage combination for UAV target tracking and obstacle avoidance.First,we independently trained the UAV target tracking and obstacle avoidance using the dueling double deep Q-network reinforcement learning algorithm.Subsequently,in a multitasking scenario,we introduce the two pre-trained networks.Meanwhile,we design a weight determined by the present risk level encountered by the UAV.This weight is utilized to perform a weighted summation of the advantage values from both networks,eliminating the need for retraining to obtain the final action.We validate our approach through extensive simulation experiments in the robotics simulator known as CoppeliaSim.The results demonstrate that our method outper-forms current state-of-the-art techniques,achieving superior performance in both tracking accuracy and avoidance of collisions. 展开更多
关键词 Multi-task obstacle avoidance reinforcement learning tracking unmanned aerial vehicles(UAV)
暂未订购 下载PDF
Autonomous Obstacle Avoidance Decision Mechanism of Intelligent Robot Based on Multimodal Perception 认领 引用
7
作者 Jiaming Yan 《Journal of Electronic Research and Application》 2025年第6期218-223,共6页
Intelligent robots are increasingly being deployed across industries ranging from manufacturing to household applications and outdoor exploration.Their autonomous obstacle avoidance capabilities in complex environment... Intelligent robots are increasingly being deployed across industries ranging from manufacturing to household applications and outdoor exploration.Their autonomous obstacle avoidance capabilities in complex environments have become a critical factor determining operational stability.Multimodal perception technology,which integrates visual,auditory,tactile,and LiDAR data,provides robots with comprehensive environmental awareness.By establishing efficient autonomous obstacle avoidance decision-making mechanisms based on this information,the system’s adaptability to challenging scenarios can be significantly enhanced.This study investigates the integration of multimodal perception with autonomous obstacle avoidance decision-making,analyzing the acquisition and processing of perceptual information,core modules and logic of decision-making mechanisms,and proposing optimization strategies for specific scenarios.The research aims to provide theoretical references for advancing autonomous obstacle avoidance technology in intelligent robots,enabling safer and more flexible movement in diverse environments. 展开更多
关键词 Multimodal perception Intelligent robot Autonomous obstacle avoidance Decision-making mechanism Environmental cognition
暂未订购 下载PDF
A Scalable Adaptive Approach to Multi-Vehicle Formation Control with Obstacle Avoidance 认领 引用 被引量：20
8
作者 Xiaohua Ge Qing-Long Han +1 位作者 Jun Wang Xian-Ming Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2022年第6期990-1004,共15页
This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multivehicle systems(MVSs)in complex obstacle-laden environments.The MVS under consideration consists of a leader v... This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multivehicle systems(MVSs)in complex obstacle-laden environments.The MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles,connected via a directed interaction topology,subject to simultaneous unknown heterogeneous nonlinearities and external disturbances.The central aim is to achieve effective and collisionfree formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering,while not demanding global information of the interaction topology.Toward this goal,a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle avoidance.Furthermore,a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is developed.It is proved that,with the proposed protocol,the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is guaranteed.Comprehensive simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach. 展开更多
关键词 Adaptive control collision avoidance distributed formation control multi-vehicle systems neural networks obstacle avoidance repulsive potential
暂未订购 下载PDF
Obstacle Avoidance of Flapping⁃Wing Air Vehicles Based on Optical Flow and Fuzzy Control 认领 引用 被引量：19
9
作者 FU Qiang WANG Jin +2 位作者 GONG Le WANG Jingyuan HE Wei 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2021年第2期206-215,共10页
The flapping-wing air vehicle(FWAV)is a kind of bio-inspired robot whose wings can flap up and down like bird and insect wings.A vision-based obstacle avoidance method for FWAVs is proposed in this paper.First,the Far... The flapping-wing air vehicle(FWAV)is a kind of bio-inspired robot whose wings can flap up and down like bird and insect wings.A vision-based obstacle avoidance method for FWAVs is proposed in this paper.First,the Farneback algorithm is used to calculate the optical flow field of the first-view video frames taken by the on-board image transmission camera.Based on the optical flow information,a fuzzy obstacle avoidance controller is then designed to generate the FWAV steering commands.Experimental results show that the proposed obstacle avoidance method can accurately identify obstacles and achieve obstacle avoidance for FWAVs. 展开更多
关键词 dense optical flow monocular vision obstacle avoidance flapping-wing air vehicle fuzzy control
暂未订购 下载PDF
A newly bio-inspired path planning algorithm for autonomous obstacle avoidance of UAV 认领 引用 被引量：21
10
作者 Yaoming ZHOU Yu SU +1 位作者 Anhuan XIE Lingyu KONG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2021年第9期199-209,共11页
In this paper,a bio-inspired path planning algorithm in 3 D space is proposed.The algorithm imitates the basic mechanisms of plant growth,including phototropism,negative geotropism and branching.The algorithm proposed... In this paper,a bio-inspired path planning algorithm in 3 D space is proposed.The algorithm imitates the basic mechanisms of plant growth,including phototropism,negative geotropism and branching.The algorithm proposed in this paper solves the dynamic obstacle avoidance path planning problem of Unmanned Aerial Vehicle(UAV)in the case of unknown environment maps.Compared with other path planning algorithms,the algorithm has the advantages of fast path planning speed and fewer route points,and can achieve the effect of low delay real-time path planning.The feasibility of the algorithm is verified in the Gazebo simulator based on the Robot Operating System(ROS)platform.Finally,an actual UAV autonomous obstacle avoidance path planning experimental platform is built,and a UAV obstacle avoidance path planning flight test is carried out based on this actual environment. 展开更多
关键词 Obstacle avoidance Path planning Plant growth ROS UAV
暂未订购 下载PDF
Distributed formation control for a multi-agent system with dynamic and static obstacle avoidances 认领 引用 被引量：14
11
作者 曹建福 凌志浩 +1 位作者 袁宜峰 高冲 《Chinese Physics B》 SCIE EI CAS CSCD 2014年第7期337-342,共6页
Formation control and obstacle avoidance for multi-agent systems have attracted more and more attention. In this paper, the problems of formation control and obstacle avoidance are investigated by means of a consensus... Formation control and obstacle avoidance for multi-agent systems have attracted more and more attention. In this paper, the problems of formation control and obstacle avoidance are investigated by means of a consensus algorithm. A novel distributed control model is proposed for the multi-agent system to form the anticipated formation as well as achieve obstacle avoidance. Based on the consensus algorithm, a distributed control function consisting of three terms （formation control term, velocity matching term, and obstacle avoidance term） is presented. By establishing a novel formation control matrix, a formation control term is constructed such that the agents can converge to consensus and reach the anticipated formation. A new obstacle avoidance function is developed by using the modified potential field approach to make sure that obstacle avoidance can be achieved whether the obstacle is in a dynamic state or a stationary state. A velocity matching term is also put forward to guarantee that the velocities of all agents converge to the same value. Furthermore, stability of the control model is proven. Simulation results are provided to demonstrate the effectiveness of the proposed control. 展开更多
关键词 multi-agent system formation control obstacle avoidance consensus theory
暂未订购 下载PDF
Improved RRT∗Algorithm for Automatic Charging Robot Obstacle Avoidance Path Planning in Complex Environments 认领 引用 被引量：11
12
作者 Chong Xu Hao Zhu +2 位作者 Haotian Zhu Jirong Wang Qinghai Zhao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第12期2567-2591,共25页
A new and improved RRT∗algorithm has been developed to address the low efficiency of obstacle avoidance planning and long path distances in the electric vehicle automatic charging robot arm.This algorithm enables the ... A new and improved RRT∗algorithm has been developed to address the low efficiency of obstacle avoidance planning and long path distances in the electric vehicle automatic charging robot arm.This algorithm enables the robot to avoid obstacles,find the optimal path,and complete automatic charging docking.It maintains the global completeness and path optimality of the RRT algorithmwhile also improving the iteration speed and quality of generated paths in both 2D and 3D path planning.After finding the optimal path,the B-sample curve is used to optimize the rough path to create a smoother and more optimal path.In comparison experiments,the new algorithmyielded reductions of 35.5%,29.2%,and 11.7%in search time and 22.8%,19.2%,and 9%in path length for the 3D environment.Finally,experimental validation of the automatic charging of electric vehicles was conducted to further verify the effectiveness of the algorithm.The simulation experimental validation was carried out by kinematic modeling and building an experimental platform.The error between the experimental results and the simulation results is within 10%.The experimental results show the effectiveness and practicality of the algorithm. 展开更多
关键词 Path planning RRT∗ deep learning obstacle avoidance
暂未订购 下载PDF
Linear-quadratic and norm-bounded combined differential game guidance scheme with obstacle avoidance for attacking defended aircraft in three-player engagement 认领 引用 被引量：5
13
作者 Xintao Wang Ming Yang +2 位作者 Songyan Wang Mingzhe Hou Tao Chao 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2024年第12期136-155,共20页
A differential game guidance scheme with obstacle avoidance,based on the formulation of a combined linear quadratic and norm-bounded differential game,is designed for a three-player engagement scenario,which includes ... A differential game guidance scheme with obstacle avoidance,based on the formulation of a combined linear quadratic and norm-bounded differential game,is designed for a three-player engagement scenario,which includes a pursuer,an interceptor,and an evader.The confrontation between the players is divided into four phases(P1-P4)by introducing the switching time,and proposing different guidance strategies according to the phase where the static obstacle is located:the linear quadratic game method is employed to devise the guidance scheme for the energy optimization when the obstacle is located in the P1 and P3 stages;the norm-bounded differential game guidance strategy is presented to satisfy the acceleration constraint under the circumstance that the obstacle is located in the P2 and P4 phases.Furthermore,the radii of the static obstacle and the interceptor are taken as the design parameters to derive the combined guidance strategy through the dead-zone function,which guarantees that the pursuer avoids the static obstacle,and the interceptor,and attacks the evader.Finally,the nonlinear numerical simulations verify the performance of the game guidance strategy. 展开更多
关键词 Active defense aircraft Differential game theory Three-player confrontation Energy optimization Acceleration constraint Obstacle avoidance
暂未订购 下载PDF
A Novel Obstacle Avoidance Consensus Control for Multi-AUV Formation System 认领 引用 被引量：12
14
作者 Linling Wang Daqi Zhu +1 位作者 Wen Pang Chaomin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第5期1304-1318,共15页
In this paper, the fixed-time event-triggered obstacle avoidance consensus control for a multi-AUV time-varying formation system in a 3D environment is presented by using an improved artificial potential field and lea... In this paper, the fixed-time event-triggered obstacle avoidance consensus control for a multi-AUV time-varying formation system in a 3D environment is presented by using an improved artificial potential field and leader-follower strategy(IAPF-LF). Firstly, the proposed fixed-time control can achieve the desired multi-AUV formation within a fixed settling time in any initial system state. Secondly, an event-triggered communication strategy is developed to govern the communication among AUVs, and the communication energy consumption can be decremented. The time-varying formation obstacle avoidance control algorithm based on IAPF-LF is designed to avoid static and dynamic obstacles, the desired formation is maintained in the presence of external disturbances, and there is no Zeno behavior under the fixed-time event-triggered consensus control strategy.The stability of the system is proved by the Lyapunov function and inequality scaling. Finally, simulation examples and water pool experiments are reported to verify the performance of the proposed theoretical algorithms. 展开更多
关键词 Autonomous underwater vehicle(AUV) event-triggered control fixed-time consensus formation obstacle avoidance improved artificial potential field and leader-follower strategy(IAPF-LF)
暂未订购 下载PDF
Obstacle avoidance for multi-missile network via distributed coordination algorithm 认领 引用 被引量：17
15
作者 Zhao Jiang Zhou Rui 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2016年第2期441-447,共7页
A distributed coordination algorithm is proposed to enhance the engagement of the multi-missile network in consideration of obstacle avoidance. To achieve a cooperative interception, the guidance law is developed in a... A distributed coordination algorithm is proposed to enhance the engagement of the multi-missile network in consideration of obstacle avoidance. To achieve a cooperative interception, the guidance law is developed in a simple form that consists of three individual components for tar- get capture, time coordination and obstacle avoidance. The distributed coordination algorithm enables a group of interceptor missiles to reach the target simultaneously, even if some member in the multi-missile network can only collect the information from nearest neighbors. The simula- tion results show that the guidance strategy provides a feasible tool to implement obstacle avoid- ance for the multi-missile network with satisfactory accuracy of target capture. The effects of the gain parameters are also discussed to evaluate the proposed approach. 展开更多
关键词 Cooperative guidance Distributed algorithms Impact time Missile guidance Multiple missiles Obstacle avoidance Proportional navigation
暂未订购 下载PDF
Path-Following Control With Obstacle Avoidance of Autonomous Surface Vehicles Subject to Actuator Faults 认领 引用 被引量：3
16
作者 Li-Ying Hao Gege Dong +1 位作者 Tieshan Li Zhouhua Peng 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第4期956-964,共9页
This paper investigates the path-following control problem with obstacle avoidance of autonomous surface vehicles in the presence of actuator faults,uncertainty and external disturbances.Autonomous surface vehicles in... This paper investigates the path-following control problem with obstacle avoidance of autonomous surface vehicles in the presence of actuator faults,uncertainty and external disturbances.Autonomous surface vehicles inevitably suffer from actuator faults in complex sea environments,which may cause existing obstacle avoidance strategies to fail.To reduce the influence of actuator faults,an improved artificial potential function is constructed by introducing the lower bound of actuator efficiency factors.The nonlinear state observer,which only depends on measurable position information of the autonomous surface vehicle,is used to address uncertainties and external disturbances.By using a backstepping technique and adaptive mechanism,a path-following control strategy with obstacle avoidance and fault tolerance is designed which can ensure that the tracking errors converge to a small neighborhood of zero.Compared with existing results,the proposed control strategy has the capability of obstacle avoidance and fault tolerance simultaneously.Finally,the comparison results through simulations are given to verify the effectiveness of the proposed method. 展开更多
关键词 Actuator faults autonomous surface vehicle(ASVs) improved artificial potential function nonlinear state observer obstacle avoidance
暂未订购 下载PDF
Obstacle Avoidance in Multi-Agent Formation Process Based on Deep Reinforcement Learning 认领 引用 被引量：2
17
作者 JI Xiukun HAI Jintao +4 位作者 LUO Wenguang LIN Cuixia XIONG Yu OU Zengkai WEN Jiayan 《Journal of Shanghai Jiaotong university(Science)》 EI 2021年第5期680-685,共6页
To solve the problems of difficult control law design,poor portability,and poor stability of traditional multi-agent formation obstacle avoidance algorithms,a multi-agent formation obstacle avoidance method based on d... To solve the problems of difficult control law design,poor portability,and poor stability of traditional multi-agent formation obstacle avoidance algorithms,a multi-agent formation obstacle avoidance method based on deep reinforcement learning(DRL)is proposed.This method combines the perception ability of convolutional neural networks(CNNs)with the decision-making ability of reinforcement learning in a general form and realizes direct output control from the visual perception input of the environment to the action through an end-to-end learning method.The multi-agent system(MAS)model of the follow-leader formation method was designed with the wheelbarrow as the control object.An improved deep Q netwrok(DQN)algorithm(we improved its discount factor and learning efficiency and designed a reward value function that considers the distance relationship between the agent and the obstacle and the coordination factor between the multi-agents)was designed to achieve obstacle avoidance and collision avoidance in the process of multi-agent formation into the desired formation.The simulation results show that the proposed method achieves the expected goal of multi-agent formation obstacle avoidance and has stronger portability compared with the traditional algorithm. 展开更多
关键词 wheelbarrow multi-agent deep reinforcement learning(DRL) formation obstacle avoidance
暂未订购 下载PDF
Obstacle avoidance method of three-dimensional obstacle spherical cap 认领 引用 被引量：5
18
作者 YANG Xiuxia ZHANG Yi ZHOU Weiwei 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2018年第5期1058-1068,共11页
Focusing on obstacle avoidance in three-dimensional space for unmanned aerial vehicle(UAV), the direct obstacle avoidance method in dynamic space based on three-dimensional velocity obstacle spherical cap is proposed,... Focusing on obstacle avoidance in three-dimensional space for unmanned aerial vehicle(UAV), the direct obstacle avoidance method in dynamic space based on three-dimensional velocity obstacle spherical cap is proposed, which quantifies the influence of threatening obstacles through velocity obstacle spherical cap parameters. In addition, the obstacle avoidance schemes of any point on the critical curve during the multi-obstacles avoidance are given. Through prediction, the insertion point for the obstacle avoidance can be obtained and the flight path can be replanned. Taking the Pythagorean Hodograph(PH) curve trajectory re-planning as an example, the three-dimensional direct obstacle avoidance method in dynamic space is tested. Simulation results show that the proposed method can realize the online obstacle avoidance trajectory re-planning, which increases the flexibility of obstacle avoidance greatly. 展开更多
关键词 three-dimensional direct space obstacle avoidance velocity obstacle cone velocity obstacle spherical cap
暂未订购 下载PDF
Target Tracking and Obstacle Avoidance for Multi-agent Systems 认领 引用 被引量：8
19
作者 Jing Yan Xin-Ping Guan Fu-Xiao Tan 《International Journal of Automation and computing》 2010年第4期550-556,共7页
This paper considers the problems of target tracking and obstacle avoidance for multi-agent systems. To solve the problem that multiple agents cannot effectively track the target while avoiding obstacle in dynamic env... This paper considers the problems of target tracking and obstacle avoidance for multi-agent systems. To solve the problem that multiple agents cannot effectively track the target while avoiding obstacle in dynamic environment, a novel control algorithm based on potential function and behavior rules is proposed. Meanwhile, the interactions among agents are also considered. According to the state whether an agent is within the area of its neighbors＇ influence, two kinds of potential functions are presented. Meanwhile, the distributed control input of each agent is determined by relative velocities as well as relative positions among agents, target and obstacle. The maximum linear speed of the agents is also discussed. Finally, simulation studies are given to demonstrate the performance of the proposed algorithm. 展开更多
关键词 Target tracking obstacle avoidance potential function multi-agent systems
暂未订购 下载PDF
Target Tracking and Obstacle Avoidance for Multi-agent Networks with Input Constraints 认领 引用 被引量：4
20
作者 Jing Yan Xin-Ping Guan +1 位作者 Xiao-Yuan Luo Fu-Xiao Tan 《International Journal of Automation and computing》 2011年第1期46-53,共8页
In this paper, the problems of target tracking and obstacle avoidance for multi-agent networks with input constraints are investigated. When there is a moving obstacle, the control objectives are to make the agents tr... In this paper, the problems of target tracking and obstacle avoidance for multi-agent networks with input constraints are investigated. When there is a moving obstacle, the control objectives are to make the agents track a moving target and to avoid collisions among agents. First, without considering the input constraints, a novel distributed controller can be obtained based on the potential function. Second, at each sampling time, the control algorithm is optimized. Furthermore, to solve the problem that agents cannot effectively avoid the obstacles in dynamic environment where the obstacles are moving, a new velocity repulsive potential is designed. One advantage of the designed control algorithm is that each agent only requires local knowledge of its neighboring agents. Finally, simulation results are provided to verify the effectiveness of the proposed approach. 展开更多
关键词 Target tracking obstacle avoidance multi-agent networks potential function optimal control.
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 … 7 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈