针对越野非结构化环境下的地面无人平台(Unmanned ground vehicle,UGV)编队仿真系统存在功能模块不完善及算法集成测试困难等问题,为便于有效测试地面无人平台编队协同控制方法性能及其适用的任务场景,降低编队协同系统的开发成本,本文...针对越野非结构化环境下的地面无人平台(Unmanned ground vehicle,UGV)编队仿真系统存在功能模块不完善及算法集成测试困难等问题,为便于有效测试地面无人平台编队协同控制方法性能及其适用的任务场景,降低编队协同系统的开发成本,本文提出了一种基于USARSim(Unified System for Automation and Robotics Simulator)和ROS(Robot Operating System)的地面无人平台编队协同仿真系统.该仿真系统由人机交互界面、基于ROS架构的地面无人平台控制系统和基于USARSim的虚拟仿真场景三个部分组成,其测试对象为地面无人平台编队协同控制算法.通过充分利用ROS中集成的开源导航算法和USARSim中丰富的机器人及环境模型,该系统为研究地面无人平台编队协同控制算法提供了新的思路和快速验证工具.以领航者−跟随者编队控制方法为例进行该仿真系统的性能测试,实验结果表明,该仿真系统能够在外界条件一致的情况下完成对编队协同控制方法的性能测试,系统稳定可靠.展开更多
如何利用社会网络信息来寻找一个合作高效、高质量的团队,已成为热门的研究话题.但现有团队生成问题中对个体拥有技能的度量大多都采用0-1方式,而在实际应用中如何界定个体是否拥有该技能的方法会在很大程度上影响团队完成任务的效率....如何利用社会网络信息来寻找一个合作高效、高质量的团队,已成为热门的研究话题.但现有团队生成问题中对个体拥有技能的度量大多都采用0-1方式,而在实际应用中如何界定个体是否拥有该技能的方法会在很大程度上影响团队完成任务的效率.另外在目前的基于社会网络的团队生成方法研究中,计算个体间关系强度时只考虑个体间曾经合作任务的数目,并没有深入挖掘社会网络条件下个体间的社会关系类别以及个体自身的其他属性,这些因素很大程度上也会影响个体间的社会关系,进而影响个体间的团队合作.针对以上问题,该文首先给出团队生成问题的具体定义和相关概念,给出技能贡献度的定义,并利用社会网络个体间的关系类别和个体间对应社会属性相似度引入一种关系模型来进一步量化团队成员个体间的关系强度;然后根据团队的不同形式分别进行了无领导者团队生成方法的研究和有领导者团队生成方法的研究,并分别提出了MCSTFA算法(Minimum Covering Steiner-based Team Forming Algorithm)和MSCTFA算法(Minimum Set Covering-based Team Forming Algorithm)来寻找最佳无领导者团队以及提出MLDTFA算法(Minimum Leader Distance based Team Forming Algorithm)来寻找最佳领导者和最佳团队.最后,利用DBLP数据集设计和实现实验以验证上述所有方法的可行性和有效性,并从团队合作代价、团队成员数量、团队连通性以及社会网络影响因素对算法的影响对比结果等方面进行比较和分析,实验结果验证了文中所提算法的可行性和高效性.展开更多
The growth of social networks in modern information systems has enabled the collaboration of experts at a scale that was unseen before. Given a task and a graph of experts where each expert possesses some skills, we t...The growth of social networks in modern information systems has enabled the collaboration of experts at a scale that was unseen before. Given a task and a graph of experts where each expert possesses some skills, we tend to find an effective team of experts who axe able to accomplish the task. This team should consider how team members collaborate in an effective manner to perform the task as well as how efficient the team assignment is, considering each expert has the minimum required level of skill. Here, we generalize the problem in multiple perspectives. First, a method is provided to determine the skill level of each expert based on his/her skill and collaboration among neighbors. Second, the graph is aggregated to the set of skilled expert groups that are strongly correlated based on their skills as well as the best connection among them. By considering the groups, search space is significantly reduced and moreover it causes to prevent from the growth of redundant communication costs and team cardinality while assigning the team members. Third, the existing RarestFirst algorithm is extended to more generalized version, and finally the cost definition is customized to improve the efficiency of selected team. Experiments on DBLP co-authorship graph show that in terms of efficiency and effectiveness, our proposed framework is achieved well in practice.展开更多
The task assignment on the Internet has been widely applied to many areas, e.g., online labor market, online paper review and social activity organization. In this paper, we are concerned with the task assignment prob...The task assignment on the Internet has been widely applied to many areas, e.g., online labor market, online paper review and social activity organization. In this paper, we are concerned with the task assignment problem related to the online labor market, termed as CLUSTERHIRE. We improve the definition of the CLUSTERHIRE problem, and propose an efficient and effective algorithm, entitled INFLUENCE. In addition, we place a participation constraint on CLUSTERHIRE. It constrains the load of each expert in order to keep all members from overworking. For the participation-constrained CLUSTERHIRE problem, we devise two algorithms, named PROJECTFIRST and ERA. The former generates a participation- constrained team by adding experts to an initial team, and the latter generates a participation-constrained team by removing the experts with the minimum influence from the universe of experts. The experimental evaluations indicate that 1） INFLU- ENCE performs better than the state-of-the-art algorithms in terms of effectiveness and time efficiency; 2） PROJECTFIRST performs better than ERA in terms of time efficiency, yet ERA performs better than PROJECTFIRST in terms of effectiveness.展开更多
文摘针对越野非结构化环境下的地面无人平台(Unmanned ground vehicle,UGV)编队仿真系统存在功能模块不完善及算法集成测试困难等问题,为便于有效测试地面无人平台编队协同控制方法性能及其适用的任务场景,降低编队协同系统的开发成本,本文提出了一种基于USARSim(Unified System for Automation and Robotics Simulator)和ROS(Robot Operating System)的地面无人平台编队协同仿真系统.该仿真系统由人机交互界面、基于ROS架构的地面无人平台控制系统和基于USARSim的虚拟仿真场景三个部分组成,其测试对象为地面无人平台编队协同控制算法.通过充分利用ROS中集成的开源导航算法和USARSim中丰富的机器人及环境模型,该系统为研究地面无人平台编队协同控制算法提供了新的思路和快速验证工具.以领航者−跟随者编队控制方法为例进行该仿真系统的性能测试,实验结果表明,该仿真系统能够在外界条件一致的情况下完成对编队协同控制方法的性能测试,系统稳定可靠.
文摘如何利用社会网络信息来寻找一个合作高效、高质量的团队,已成为热门的研究话题.但现有团队生成问题中对个体拥有技能的度量大多都采用0-1方式,而在实际应用中如何界定个体是否拥有该技能的方法会在很大程度上影响团队完成任务的效率.另外在目前的基于社会网络的团队生成方法研究中,计算个体间关系强度时只考虑个体间曾经合作任务的数目,并没有深入挖掘社会网络条件下个体间的社会关系类别以及个体自身的其他属性,这些因素很大程度上也会影响个体间的社会关系,进而影响个体间的团队合作.针对以上问题,该文首先给出团队生成问题的具体定义和相关概念,给出技能贡献度的定义,并利用社会网络个体间的关系类别和个体间对应社会属性相似度引入一种关系模型来进一步量化团队成员个体间的关系强度;然后根据团队的不同形式分别进行了无领导者团队生成方法的研究和有领导者团队生成方法的研究,并分别提出了MCSTFA算法(Minimum Covering Steiner-based Team Forming Algorithm)和MSCTFA算法(Minimum Set Covering-based Team Forming Algorithm)来寻找最佳无领导者团队以及提出MLDTFA算法(Minimum Leader Distance based Team Forming Algorithm)来寻找最佳领导者和最佳团队.最后,利用DBLP数据集设计和实现实验以验证上述所有方法的可行性和有效性,并从团队合作代价、团队成员数量、团队连通性以及社会网络影响因素对算法的影响对比结果等方面进行比较和分析,实验结果验证了文中所提算法的可行性和高效性.
文摘The growth of social networks in modern information systems has enabled the collaboration of experts at a scale that was unseen before. Given a task and a graph of experts where each expert possesses some skills, we tend to find an effective team of experts who axe able to accomplish the task. This team should consider how team members collaborate in an effective manner to perform the task as well as how efficient the team assignment is, considering each expert has the minimum required level of skill. Here, we generalize the problem in multiple perspectives. First, a method is provided to determine the skill level of each expert based on his/her skill and collaboration among neighbors. Second, the graph is aggregated to the set of skilled expert groups that are strongly correlated based on their skills as well as the best connection among them. By considering the groups, search space is significantly reduced and moreover it causes to prevent from the growth of redundant communication costs and team cardinality while assigning the team members. Third, the existing RarestFirst algorithm is extended to more generalized version, and finally the cost definition is customized to improve the efficiency of selected team. Experiments on DBLP co-authorship graph show that in terms of efficiency and effectiveness, our proposed framework is achieved well in practice.
基金The work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61472299, 61540008, 61672417 and 61602354, the Fundamental Research Funds for the Central Universities of China under Grant No. BDY10, the Shaanxi Postdoctoral Science Foundation, and the Natural Science Basic Research Plan of Shaanxi Province of China under Grant No. 2014JQ8359.
文摘The task assignment on the Internet has been widely applied to many areas, e.g., online labor market, online paper review and social activity organization. In this paper, we are concerned with the task assignment problem related to the online labor market, termed as CLUSTERHIRE. We improve the definition of the CLUSTERHIRE problem, and propose an efficient and effective algorithm, entitled INFLUENCE. In addition, we place a participation constraint on CLUSTERHIRE. It constrains the load of each expert in order to keep all members from overworking. For the participation-constrained CLUSTERHIRE problem, we devise two algorithms, named PROJECTFIRST and ERA. The former generates a participation- constrained team by adding experts to an initial team, and the latter generates a participation-constrained team by removing the experts with the minimum influence from the universe of experts. The experimental evaluations indicate that 1） INFLU- ENCE performs better than the state-of-the-art algorithms in terms of effectiveness and time efficiency; 2） PROJECTFIRST performs better than ERA in terms of time efficiency, yet ERA performs better than PROJECTFIRST in terms of effectiveness.
