Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a ...Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation.展开更多
针对二维基于特征分解的多重信号分类(Multiple Signal Classificaion,MUSIC)算法在多谱峰搜索时计算量大、估计失败率高以及传统蚁群算法在进行二维多谱峰搜索时无法同时搜索多个谱峰的问题,将蚁群算法进行改进,同时与聚类思想相结合,...针对二维基于特征分解的多重信号分类(Multiple Signal Classificaion,MUSIC)算法在多谱峰搜索时计算量大、估计失败率高以及传统蚁群算法在进行二维多谱峰搜索时无法同时搜索多个谱峰的问题,将蚁群算法进行改进,同时与聚类思想相结合,加上动态调整搜索范围,使得改进后的蚁群算法可以进行二维MUSIC多谱峰搜索,同时可以分辨出相距较近的信号源的波达方向。通过仿真验证了改进后的蚁群算法在一定信噪比下进行谱峰搜索成功率高,鲁棒性强,且不受信号源距离大小的影响,证明了该算法适合进行多谱峰搜索的任务。展开更多
A heuristic theoretical optimal routing algorithm （TORA） is presented to achieve the data-gathering structure of location-aided quality of service （QoS） in wireless sensor networks （WSNs）. The construction of TO...A heuristic theoretical optimal routing algorithm （TORA） is presented to achieve the data-gathering structure of location-aided quality of service （QoS） in wireless sensor networks （WSNs）. The construction of TORA is based on a kind of swarm intelligence （SI） mechanism, i. e. , ant colony optimization. Firstly, the ener- gy-efficient weight is designed based on flow distribution to divide WSNs into different functional regions, so the routing selection can self-adapt asymmetric power configurations with lower latency. Then, the designs of the novel heuristic factor and the pheromone updating rule can endow ant-like agents with the ability of detecting the local networks energy status and approaching the theoretical optimal tree, thus improving the adaptability and en- ergy-efficiency in route building. Simulation results show that compared with some classic routing algorithms, TORA can further minimize the total communication energy cost and enhance the QoS performance with low-de- lay effect under the data-gathering condition.展开更多
为推动名优茶叶采摘自动化,茶叶采摘机械臂快速、高质量路径规划是实现高效采摘的关键。针对传统群智能优化算法在茶园复杂环境及约束条件下存在的路径质量差、算法耗时长及规划不稳定等问题。提出一种改进豪猪优化器(Crested Porcupine...为推动名优茶叶采摘自动化,茶叶采摘机械臂快速、高质量路径规划是实现高效采摘的关键。针对传统群智能优化算法在茶园复杂环境及约束条件下存在的路径质量差、算法耗时长及规划不稳定等问题。提出一种改进豪猪优化器(Crested Porcupine Optimizer,CPO)的机械臂路径规划方法。通过引入动态种群收缩策略,在迭代过程中缩减种群规模,减少计算成本,使用末位淘汰机制及对算法结构改良提升全局寻优能力,增加个体多样性,并引入动态调整因子λ_t改进第一防御策略,平衡算法在不同阶段的探索与优化比例。通过Lindenmayer系统及UR5机械臂构建茶叶采摘仿真场景,进行仿真路径规划实验。在10个不同环境中,改进CPO算法相比原算法,平均计算时间减少4.7%,平均路径长度缩短0.78%;与灰狼优化(Grey Wolf Optimizer,GWO)、蜣螂优化(Dung Beetle Optimizer,DBO)、快速扩展随机树(Rapidly-exploring Random Trees,RRT)等算法相比,平均耗时相较GWO、DBO分别下降25%、24%,路径长度相较RRT算法减少23%、平均规划成功率高28%。改进CPO算法相较其他算法耗时更短,同时具有更好的路径质量及规划成功率,验证了其在茶叶采摘机械臂路径规划问题上的实用价值。展开更多
To solve the weapon network system optimization problem against small raid objects with low attitude,the concept of direction probability and a new evaluation index system are proposed.By calculating the whole damagin...To solve the weapon network system optimization problem against small raid objects with low attitude,the concept of direction probability and a new evaluation index system are proposed.By calculating the whole damaging probability that changes with the defending angle,the efficiency of the whole weapon network system can be subtly described.With such method,we can avoid the inconformity of the description obtained from the traditional index systems.Three new indexes are also proposed,i.e.join index,overlap index and cover index,which help manage the relationship among several sub-weapon-networks.By normalizing the computation results with the Sigmoid function,the matching problem between the optimization algorithm and indexes is well settled.Also,the algorithm of improved marriage in honey bees optimization that proposed in our previous work is applied to optimize the embattlement problem.Simulation is carried out to show the efficiency of the proposed indexes and the optimization algorithm.展开更多
The particle swarm optimization (PSO) algorithm is introduced to deal with some open anti-windup problems, i.e., determining the initial condition when applying the iterative algorithm to enlarge the estimate of the d...The particle swarm optimization (PSO) algorithm is introduced to deal with some open anti-windup problems, i.e., determining the initial condition when applying the iterative algorithm to enlarge the estimate of the domain of attraction, determining the design point in the delayed anti-windup scheme, and determining the design point and the weighting factors in the multi-stage anti-windup scheme. Therefore, the corresponding PSO-based algorithms are proposed. Unlike the traditional methods in which the free design parameters can only be selected by trial and error with the available computational results, the PSO-based algorithms provide a systematic way to determine these parameters. In addition, the algorithms are easy to be implemented and are very likely to find the desirable parameters that further improve the anti-windup closed-loop performances. Simulation results are presented to validate the effectiveness and advantages of the proposed method.展开更多
文摘Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation.
文摘针对二维基于特征分解的多重信号分类(Multiple Signal Classificaion,MUSIC)算法在多谱峰搜索时计算量大、估计失败率高以及传统蚁群算法在进行二维多谱峰搜索时无法同时搜索多个谱峰的问题,将蚁群算法进行改进,同时与聚类思想相结合,加上动态调整搜索范围,使得改进后的蚁群算法可以进行二维MUSIC多谱峰搜索,同时可以分辨出相距较近的信号源的波达方向。通过仿真验证了改进后的蚁群算法在一定信噪比下进行谱峰搜索成功率高,鲁棒性强,且不受信号源距离大小的影响,证明了该算法适合进行多谱峰搜索的任务。
基金Supported by the Foundation of National Natural Science of China(60802005,50803016)the Science Foundation for the Excellent Youth Scholars in East China University of Science and Technology(YH0157127)the Undergraduate Innovational Experimentation Program in East China University of Science andTechnology(X1033)~~
文摘A heuristic theoretical optimal routing algorithm （TORA） is presented to achieve the data-gathering structure of location-aided quality of service （QoS） in wireless sensor networks （WSNs）. The construction of TORA is based on a kind of swarm intelligence （SI） mechanism, i. e. , ant colony optimization. Firstly, the ener- gy-efficient weight is designed based on flow distribution to divide WSNs into different functional regions, so the routing selection can self-adapt asymmetric power configurations with lower latency. Then, the designs of the novel heuristic factor and the pheromone updating rule can endow ant-like agents with the ability of detecting the local networks energy status and approaching the theoretical optimal tree, thus improving the adaptability and en- ergy-efficiency in route building. Simulation results show that compared with some classic routing algorithms, TORA can further minimize the total communication energy cost and enhance the QoS performance with low-de- lay effect under the data-gathering condition.
文摘为推动名优茶叶采摘自动化,茶叶采摘机械臂快速、高质量路径规划是实现高效采摘的关键。针对传统群智能优化算法在茶园复杂环境及约束条件下存在的路径质量差、算法耗时长及规划不稳定等问题。提出一种改进豪猪优化器(Crested Porcupine Optimizer,CPO)的机械臂路径规划方法。通过引入动态种群收缩策略,在迭代过程中缩减种群规模,减少计算成本,使用末位淘汰机制及对算法结构改良提升全局寻优能力,增加个体多样性,并引入动态调整因子λ_t改进第一防御策略,平衡算法在不同阶段的探索与优化比例。通过Lindenmayer系统及UR5机械臂构建茶叶采摘仿真场景,进行仿真路径规划实验。在10个不同环境中,改进CPO算法相比原算法,平均计算时间减少4.7%,平均路径长度缩短0.78%;与灰狼优化(Grey Wolf Optimizer,GWO)、蜣螂优化(Dung Beetle Optimizer,DBO)、快速扩展随机树(Rapidly-exploring Random Trees,RRT)等算法相比,平均耗时相较GWO、DBO分别下降25%、24%,路径长度相较RRT算法减少23%、平均规划成功率高28%。改进CPO算法相较其他算法耗时更短,同时具有更好的路径质量及规划成功率,验证了其在茶叶采摘机械臂路径规划问题上的实用价值。
基金Sponsored by Beijing Priority Laboratory Fund of China(SYS10070522)
文摘To solve the weapon network system optimization problem against small raid objects with low attitude,the concept of direction probability and a new evaluation index system are proposed.By calculating the whole damaging probability that changes with the defending angle,the efficiency of the whole weapon network system can be subtly described.With such method,we can avoid the inconformity of the description obtained from the traditional index systems.Three new indexes are also proposed,i.e.join index,overlap index and cover index,which help manage the relationship among several sub-weapon-networks.By normalizing the computation results with the Sigmoid function,the matching problem between the optimization algorithm and indexes is well settled.Also,the algorithm of improved marriage in honey bees optimization that proposed in our previous work is applied to optimize the embattlement problem.Simulation is carried out to show the efficiency of the proposed indexes and the optimization algorithm.
文摘The particle swarm optimization (PSO) algorithm is introduced to deal with some open anti-windup problems, i.e., determining the initial condition when applying the iterative algorithm to enlarge the estimate of the domain of attraction, determining the design point in the delayed anti-windup scheme, and determining the design point and the weighting factors in the multi-stage anti-windup scheme. Therefore, the corresponding PSO-based algorithms are proposed. Unlike the traditional methods in which the free design parameters can only be selected by trial and error with the available computational results, the PSO-based algorithms provide a systematic way to determine these parameters. In addition, the algorithms are easy to be implemented and are very likely to find the desirable parameters that further improve the anti-windup closed-loop performances. Simulation results are presented to validate the effectiveness and advantages of the proposed method.
