人工智能集群控制演示验证系统
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摘要
为了对人工智能集群的自组织控制方法进行验证,提出了一种在实验室环境下低成本建立人工智能集群控制演示验证系统的方法.系统由演示场、多个移动个体、合作标识及识别单元、控制及信息分配单元共同组成.合作标识及识别单元能准确获取多个个体的身份信息和高精度的位姿信息.控制及信息分配单元对智能集群中个体间的信息交换和个体控制策略进行模拟.最后,以机动自组织探测集群为验证对象,在人工智能集群控制演示验证系统中对基于人工势场法的自组织控制策略进行演示验证.验证结果表明,该智能集群控制演示验证系统能够在实验室环境下对智能集群的运行过程进行演示和验证,可以更真实地体现智能集群的实际表现.
A low-cost demonstration and verification system is developed and established in laboratory condition to demonstrate and verify the self-organizing control strategy of an intelligent swarm. The system consists of an arena, multiple mobile individuals, the cooperative identification logo and the identification unit, and the control and information allocating unit. The cooperative identification and the identification unit provides the accurate identification and the high-precision position and direction data of the multiple mobile individuals. The control and information allocating unit simulates the rules of the information exchanging and control among the individuals of an intelligent swarm. Finally, the mobile self-organized detecting swarm is taken as a demonstration case, and the self-organizing control strategy based on artificial potential field is demonstrated and verified in the system. The demonstration results show that the proposed system can demonstrate and verify the operation process of an intelligent swarm in laboratory condition, and the demonstration can provide the actual performance of an intelligent swarm.
A low-cost demonstration and verification system is developed and established in laboratory condition to demonstrate and verify the self-organizing control strategy of an intelligent swarm. The system consists of an arena, multiple mobile individuals, the cooperative identification logo and the identification unit, and the control and information allocating unit. The cooperative identification and the identification unit provides the accurate identification and the high-precision position and direction data of the multiple mobile individuals. The control and information allocating unit simulates the rules of the information exchanging and control among the individuals of an intelligent swarm. Finally, the mobile self-organized detecting swarm is taken as a demonstration case, and the self-organizing control strategy based on artificial potential field is demonstrated and verified in the system. The demonstration results show that the proposed system can demonstrate and verify the operation process of an intelligent swarm in laboratory condition, and the demonstration can provide the actual performance of an intelligent swarm.
引文
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[2]S?ahin E.Swarm robotics:From sources of inspiration to domains of application[M]//Swarm Robotics.Berlin,Germany:Springer,2005.10-20.
[3]Blum C,Merkle D.Swarm intelligence:Introduction and applications[M].Berlin,Germany:Springer,2008.
[4]Deneubourg J L,Aron S,Goss S,et al.The self-organizing exploratory pattern of the argentine ant[J].Journal of Insect Behavior,1990,3(2):159-168.
[5]Dorigo M,Birattari M,Stutzle T.Ant colony optimization[J].Computational Intelligence Magazine,2006,1(4):28-39.
[6]Merkle D,Middendorf M,Schmeck H.Ant colony optimization for resource-constrained project scheduling[J].IEEE Transactions on Evolutionary Computation,2002,6(4):333-346.
[7]Dorigo M,Blum C.Ant colony optimization theory:A survey[J].Theoretical Computer Science,2005,344(2-3):243-278.
[8]Castello E,Yamamoto T,Nakamura Y,et al.Task allocation for a robotic swarm based on an adaptive response threshold model[C]//13th International Conference on Control,Automation and Systems.Piscataway,USA:IEEE,2013:259-266.
[9]Pini G,Brutschy A,Pinciroli C,et al.Autonomous task partitioning in robot foraging:An approach based on cost estimation[J].Adaptive Behavior,2013,21(2):118-136.
[10]Matari′c M J.Behavior-based robotics as a tool for synthesis of artificial behavior and analysis of natural behavior[J].Trends in Cognitive Sciences,1998,2(3):82-87.
[11]Verhoeven C J M,Bentum M J,Monna G L E,et al.On the origin of satellite swarms[J].Acta Astronautica,2011,68(7-8):1392-1395.
[12]D’Arrigo P,Santandrea S.The APIES mission to explore the asteroid belt[J].Advances in Space Research,2006,38(9):2060-2067.
[13]Curtis S A,Truszkowski W,Rilee M L,et al.ANTS for human exploration and development of space[C]//IEEE Aerospace Conference.Piscataway,USA:IEEE,2003:1-261.
[14]Truszkowski W,Hallock H,Rouff C,et al.Autonomous and autonomic systems:With applications to NASA intelligent spacecraft operations and exploration systems[M].Berlin,Germany:Springer,2010.
[15]王浩,丁磊,方宝富,等.多机器人追逃问题中的追捕联盟生成算法[J].机器人,2013,35(2):142-150.Wang H,Ding L,Fang B F,et al.Pursuers-coalition construction algorithm in multi-robot pursuit-evasion game[J].Robot,2013,35(2):142-150.
[16]黄天云,陈雪波,徐望宝,等.基于松散偏好规则的群体机器人系统自组织协作围捕[J].自动化学报,2013,39(1):57-68.Huang T Y,Chen X B,Xu W B,et al.A self-organizing cooperative hunting by swarm robotic systems based on loosepreference rule[J].Acta Automatica Sinica,2013,39(1):57-68.
[17]Ducatelle F,Di Caro G A,F¨orster A,et al.Cooperative navigation in robotic swarms[J].Swarm Intelligence,2014,8(1):1-33.
[18]Fujisawa R,Dobata S,Sugawara K,et al.Designing pheromone communication in swarm robotics:Group foraging behavior mediated by chemical substance[J].Swarm Intelligence,2014,8(3):227-246.
[19]An M Y,Fan L,Wang Z K,et al.Design for autonomous selforganizing target detection system based on artificial swarms[C]//2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems.Piscataway,USA:IEEE,2014.
[20]Gazi V,Passino K M.Stability analysis of swarms[J].IEEE Transactions on Automatic Control,2003,48(4):692-697.
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