期刊文献+
共找到6篇文章
< 1 >
每页显示 20 50 100
Multi-UAV Collaborative Trajectory Planning for 3D Terrain Based on CS-GJO Algorithm
1
作者 Taishan Lou Yu Wang +1 位作者 Zhepeng Yue Liangyu Zhao 《Complex System Modeling and Simulation》 EI 2024年第3期274-291,共18页
Existing solutions for collaborative trajectory planning using multiple UAVs suffer from issues such as low accuracy,instability,and slow convergence.To address the aforementioned issues,this paper introduces a new me... Existing solutions for collaborative trajectory planning using multiple UAVs suffer from issues such as low accuracy,instability,and slow convergence.To address the aforementioned issues,this paper introduces a new method for multiple unmanned aerial vehicle(UAV)3D terrain cooperative trajectory planning based on the cuck0o search golden jackal optimization(CS-GJO)algorithm.A model for single UAV trajectory planning and a model for multi-UAV collaborative trajectory planning have been developed,and the problem of solving the models is restructured into an optimization problem.Building upon the original golden jackal optimization,the use of tent chaotic mapping aids in the generation of the golden jackal's inital population,thereby promoting population diversity.Subsequently,the position update strategy of the cuckoo search algorithm is combined for purpose of update the position information of individual golden jackals,effectively preventing the algorithm from getting stuck in local minima.Finally,the corresponding nonlinear control parameter were developed.The new parameters expedite the decrease in the convergence factor during the pre-exploration stage,resulting in an improved overall search speed of the algorithm.Moreover,they attenuate the decrease in the convergence factor during the post-exploration stage,thereby enhancing the algorithm's global search.The experimental results demonstrate that the CS-GJO algorithm efficiently and accurately accomplishes multi-UAV cooperative trajectory planning in a 3D environment.Compared with other comparative algorithms,the CS-GJO algorithm also has better stability,higher optimization accuracy,and faster convergence speed. 展开更多
关键词 golden jackal optimization multiple unmanned aerial vehicle(multiUAV)collaboration 3D track planning tent chaos mapping cuckoo search
原文传递
Hierarchical Decision-Making Framework for Multi-UAV Task Assignment via Enhanced Pigeon-Inspired Optimization
2
作者 Weike Chen Xingshuo Hai +2 位作者 Yanming Hu Qiang Feng Zili Wang 《Guidance, Navigation and Control》 2023年第4期1-25,共25页
Effective task assignment decisions are paramount for ensuring reliable task execution in multi-UAV systems.However,in the development of feasible plans,challenges stemming from extensive and prolonged task requiremen... Effective task assignment decisions are paramount for ensuring reliable task execution in multi-UAV systems.However,in the development of feasible plans,challenges stemming from extensive and prolonged task requirements are encountered.This paper establishes a decision-making framework for multiple unmanned aerial vehicles(multi-UAV)based on the well-known pigeon-inspired optimization(PIO)algorithm.By addressing the problem from a hierarchical structural perspective,the initial stage involves minimizing the global objective of the flight distance cost after obtaining the entire task distribution and task requirements,utilizing the global optimization capability of the classical PIO algorithm to allocate feasible task spaces for each UAV.In the second stage,building upon the decisions made in the preceding stage,each UAV is abstracted as an agent maximizing its own task execution benefits.An improved version of the PIO algorithm modified with a sine-cosine search mechanism is proposed,enabling the acquisition of the optimal task execution sequence.Simulation experiments involving two different scales of UAVs validate the effectiveness of the proposed methodology.Moreover,dynamic events such as UAV damage and task changes are considered in the simulation to validate the efficacy of the two-stage framework. 展开更多
关键词 DECISION-MAKING multiple unmanned aerial vehicles(multi-UAV) pigeon-inspired optimization(PIO) task assignment
原文传递
Consensus-based three-dimensional multi-UAV formation control strategy with high precision 被引量:19
3
作者 Mao-de YAN Xu ZHU +1 位作者 Xun-xun ZHANG Yao-hong QU 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2017年第7期968-977,共10页
We propose a formation control strategy for multiple unmanned aerial vehicles (multi-UAV) based on second-order consensus, by introducing position and velocity coordination variables through neighbor-to-neighbor int... We propose a formation control strategy for multiple unmanned aerial vehicles (multi-UAV) based on second-order consensus, by introducing position and velocity coordination variables through neighbor-to-neighbor interaction to generate steering commands. A cooperative guidance algorithm and a cooperative control algorithm are proposed together to maintain a specified geometric configuration, managing the position and attitude respectively. With the whole system composed of the six-degree-of-freedom UAV model, tile cooperative guidance algorithm, and the cooperative control algorithm, the formation control strategy is a closed-loop one and with full states. The cooperative guidance law is a second-order consensus algorithm, providing the desired acceleration, pitch rate, and heading rate. Longitudinal and lateral motions are jointly considered, and the cooperative control law is designed by deducing state equations. Closed-loop stability of the formation is analyzed, and a necessary and sufficient condition is provided. Measurement errors in position data are suppressed by synchronization technology to improve the control precision. In the simulation, three-dimensional formation flight demonstrates the feasibility and effectiveness of the formation control strategy. 展开更多
关键词 multiple unmanned aerial vehicles CONSENSUS Cooperative guidance Cooperative control Synchronization technology
原文传递
Periodic event-triggered formation control for multi-UAV systems with collision avoidance 被引量:6
4
作者 Tong WU Jie WANG Bailing TIAN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2022年第8期193-203,共11页
In this paper,periodic event-triggered formation control problems with collision avoidance are studied for leader–follower multiple Unmanned Aerial Vehicles(UAVs).Firstly,based on the Artificial Potential Field(APF)m... In this paper,periodic event-triggered formation control problems with collision avoidance are studied for leader–follower multiple Unmanned Aerial Vehicles(UAVs).Firstly,based on the Artificial Potential Field(APF)method,a novel sliding manifold is proposed for controller design,which can solve the problem of collision avoidance.Then,the event-triggered strategy is applied to the distributed formation control of multi-UAV systems,where the evaluation of the event condition is continuous.In addition,the exclusion of Zeno behavior can be guaranteed by the inter-event time between two successive trigger events have a positive lower bound.Next,a periodic event-triggered mechanism is developed for formation control based on the continuous eventtriggered mechanism.The periodic trigger mechanism does not need additional hardware circuits and sophisticated sensors,which can reduce the control cost.The stability of the control system is proved by the Lyapunov function method.Finally,some numerical simulations are presented to illustrate the effectiveness of the proposed control protocol. 展开更多
关键词 Collision avoidance Distributed formation control Event-triggered strategy Leader-follower method multiple unmanned aerial vehicles(UAVs)
原文传递
Energy-efficient trajectory planning for amulti-UAV-assisted mobile edge computing system 被引量:3
5
作者 Pei-qiu HUANG Yong WANG Ke-zhi WANG 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2020年第12期1713-1725,共13页
We study a mobile edge computing system assisted by multiple unmanned aerial vehicles(UAVs),where the UAVs act as edge servers to provide computing services for Internet of Things devices.Our goal is to minimize the e... We study a mobile edge computing system assisted by multiple unmanned aerial vehicles(UAVs),where the UAVs act as edge servers to provide computing services for Internet of Things devices.Our goal is to minimize the energy consumption of this system by planning the trajectories of UAVs.This problem is difficult to address because when planning the trajectories,we need to consider not only the order of stop points(SPs),but also their deployment(including the number and locations)and the association between UAVs and SPs.To tackle this problem,we present an energy-efficient trajectory planning algorithm(TPA)which comprises three phases.In the first phase,a differential evolution algorithm with a variable population size is adopted to update the number and locations of SPs at the same time.In the second phase,the k-means clustering algorithm is employed to group the given SPs into a set of clusters,where the number of clusters is equal to th at of UAVs and each cluster contains all SPs visited by the same UAV.In the third phase,to quickly generate the trajectories of UAVs,we propose a low-complexity greedy method to construct the order of SPs in each cluster.Compared with other algorithms,the effectiveness of TPA is verified on a set of instances at different scales. 展开更多
关键词 multiple unmanned aerial vehicles Mobile edge computing Trajectory planning Differential evolution k-means clustering algorithm Greedy method
原文传递
Multi-UAV Cooperative Trajectory Planning Based on Many-Objective Evolutionary Algorithm 被引量:4
6
作者 Hui Bai Tian Fan +1 位作者 Yuan Niu Zhihua Cui 《Complex System Modeling and Simulation》 2022年第2期130-141,共12页
The trajectory planning of multiple unmanned aerial vehicles(UAVs)is the core of efficient UAV mission execution.Existing studies have mainly transformed this problem into a single-objective optimization problem using... The trajectory planning of multiple unmanned aerial vehicles(UAVs)is the core of efficient UAV mission execution.Existing studies have mainly transformed this problem into a single-objective optimization problem using a single metric to evaluate multi-UAV trajectory planning methods.However,multi-UAV trajectory planning evolves into a many-objective optimization problem due to the complexity of the demand and the environment.Therefore,a multi-UAV cooperative trajectory planning model based on many-objective optimization is proposed to optimize trajectory distance,trajectory time,trajectory threat,and trajectory coordination distance costs of UAVs.The NSGA-III algorithm,which overcomes the problems of traditional trajectory planning,is used to solve the model.This paper also designs a segmented crossover strategy and introduces dynamic crossover probability in the crossover operator to improve the solving efficiency of the model and accelerate the convergence speed of the algorithm.Experimental results prove the effectiveness of the multi-UAV cooperative trajectory planning algorithm,thereby addressing different actual needs. 展开更多
关键词 multiple unmanned aerial vehicles(multi-UAV) coordinated trajectory planning NSGA-III many-objective optimization
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部