The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper coo...The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments.展开更多
Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Fir...Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Firstly,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation.Then,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed.Finally,several simulations are carried out to illustrate superiority and effectiveness.展开更多
A lower-part humanoid robot CHP-1 with 12 degree-of-freedom of motion has been developed for cooperative motion,such as pushing or lifting an object.The capability of the robot is mainly dependent on the performance o...A lower-part humanoid robot CHP-1 with 12 degree-of-freedom of motion has been developed for cooperative motion,such as pushing or lifting an object.The capability of the robot is mainly dependent on the performance of the motors,thus the motors need to be properly selected.For the purpose,the kinematics of the robot was analyzed,and a number of simulations for two kinds of cooperative motions were carried out.The torques required at each motor of the robot under external forces were obtained.Here,the external forces were also estimated through simulation and literature survey.On the basis of the torques found,the selection of motors was finally suggested,and the motors are to be installed to the humanoid robot.展开更多
为解决城市"复杂巨系统"组织运行中的人-机-物协同融合等共性问题,构建面向智慧城市应用服务的通用平台,结合智慧系统基础理论研究,通过对IBM(International Business Machines Corporation)、阿里云计算有限公司、华为技术...为解决城市"复杂巨系统"组织运行中的人-机-物协同融合等共性问题,构建面向智慧城市应用服务的通用平台,结合智慧系统基础理论研究,通过对IBM(International Business Machines Corporation)、阿里云计算有限公司、华为技术有限公司及北京易华录信息技术股份有限公司等中外企业智慧系统在智慧城市中的实际应用分析,明确了智慧系统的基本内涵与特征,提出了智慧系统的通用组成,开展了智慧系统体系架构的设计,并从感、传、智、用4个系统单元设计技术和一个系统总体设计技术角度,全面阐述了智慧系统面向智慧城市应用的前沿技术体系构成及其应用方向,为智慧系统的设计提供了技术指引和系统构建参考。展开更多
基金Project(61801495)supported by the National Natural Science Foundation of China
文摘The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments.
文摘Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Firstly,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation.Then,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed.Finally,several simulations are carried out to illustrate superiority and effectiveness.
基金Project supported by the Second Stage of Brain Korea 21 Projects
文摘A lower-part humanoid robot CHP-1 with 12 degree-of-freedom of motion has been developed for cooperative motion,such as pushing or lifting an object.The capability of the robot is mainly dependent on the performance of the motors,thus the motors need to be properly selected.For the purpose,the kinematics of the robot was analyzed,and a number of simulations for two kinds of cooperative motions were carried out.The torques required at each motor of the robot under external forces were obtained.Here,the external forces were also estimated through simulation and literature survey.On the basis of the torques found,the selection of motors was finally suggested,and the motors are to be installed to the humanoid robot.
文摘为解决城市"复杂巨系统"组织运行中的人-机-物协同融合等共性问题,构建面向智慧城市应用服务的通用平台,结合智慧系统基础理论研究,通过对IBM(International Business Machines Corporation)、阿里云计算有限公司、华为技术有限公司及北京易华录信息技术股份有限公司等中外企业智慧系统在智慧城市中的实际应用分析,明确了智慧系统的基本内涵与特征,提出了智慧系统的通用组成,开展了智慧系统体系架构的设计,并从感、传、智、用4个系统单元设计技术和一个系统总体设计技术角度,全面阐述了智慧系统面向智慧城市应用的前沿技术体系构成及其应用方向,为智慧系统的设计提供了技术指引和系统构建参考。
