基于认知差异的多机器人协同信息趋向烟羽源搜索方法
|
摘要
多机器人协同稀疏烟羽源搜索研究中,追求群体信息一致而忽视个体独立搜索能力的发挥,导致群体无法有效适应复杂搜索状况.为此,提出一种基于认知差异的协同信息趋向源搜索方法.首先,利用相对熵度量群内个体对源位置估计的认知差异;然后,据此赋予不同个体烟羽采样以相应权重,在贝叶斯推理过程自适应权衡自身线索与群体线索;最后,采用分布式信息熵决策实施协同信息趋向搜索.多种场景下的仿真结果验证了所提出算法的优越性.
In multi-robot plume source searching with sporadic cues, the classic approaches strive for achieving social information consistency of all robots while the exploration ability of individual robot is ignored, which weakens the adaptivity of the group in complex environment. To overcome this drawback, a cooperative infotaxis searching approach is proposed. The relative entropy is introduced to measure the cognitive differences of likelihood function of source location between robots. Then, different weights are assigned to the sensor measurements of individual robot based on the cognitive differences. In the Bayesian learning process, the trade-off between individual cues and social cues is adaptively regulated for acquiring private source location probability distribution. Finally, the collaborative infotaxis search strategy is implemented by performing an entropy decision of each robot. The advantages of the proposed method are illustrated by simulation experiments under different scenarios.
In multi-robot plume source searching with sporadic cues, the classic approaches strive for achieving social information consistency of all robots while the exploration ability of individual robot is ignored, which weakens the adaptivity of the group in complex environment. To overcome this drawback, a cooperative infotaxis searching approach is proposed. The relative entropy is introduced to measure the cognitive differences of likelihood function of source location between robots. Then, different weights are assigned to the sensor measurements of individual robot based on the cognitive differences. In the Bayesian learning process, the trade-off between individual cues and social cues is adaptively regulated for acquiring private source location probability distribution. Finally, the collaborative infotaxis search strategy is implemented by performing an entropy decision of each robot. The advantages of the proposed method are illustrated by simulation experiments under different scenarios.
引文
[1]孟庆浩,李飞.主动嗅觉研究现状[J].机器人,2006,28(1):89-96.(Meng Q H,Li F.Review of active olfaction[J].Robot,2006,28(1):89-96.)
[2]Kowadlo G,Russell R A.Robot odor localization:A taxonomy and survey[J].The Int J of Robotics Research,2008,27(8):869-894.
[3]Umbers K L,Symonds M E,Kokko H.The mothematics of female pheromone signaling:Strategies for aging virgins[J].The American Naturalist,2015,185(3):417-432.
[4]Gardiner J M,Atema J.The function of bilateral odor arrival time differences in olfactory orientation of sharks[J].Current Biology,2010,20(13):1187-1191.
[5]Naeem W,Sutton R,Chudley J.Chemical plume tracing and odour source localisation by autonomous vehicles[J].J of Navigation,2007,60(2):173-190.
[6]CardéR T,Willis M A.Navigational strategies used by insects to find distant,wind-borne sources of odor[J].J ofChemical Ecology,2008,34(7):854-866.
[7]Webster D R,Volyanskyy K Y,Weissburg M J.Bioinspired algorithm for autonomous sensor-driven guidance in turbulent chemical plumes[J].Bioinspiration and Biomimetics,2012,7(3):129-135.
[8]Tishby N,Polani D.Information theory of decisions and actions[M].New York:Springer,2011.
[9]Vergassola M,Villermaux E,Shraiman I.‘Infotaxis’as a strategy for searching without gradients[J].Nature,2007,445(7126):406-409.
[10]Rodríguez J D,Gómez-Ullate D,Mejía-Monasterio C.Limits on the performance of infotaxis under inaccurate modelling of the environment[J].Ar Xiv Preprint,2014,1408(1873):1-8.
[11]Null N.Correction:Reactive searching and infotaxis in odor source localization[J].Plos Computational Biology,2014,10(10):e1003861-e1003861.
[12]Moraud E M,Martinez D.Effectiveness and robustness of robot infotaxis for searching in dilute conditions[J].Frontiers in Neurorobotics,2010,4(4):1-8.
[13]Ristic B,Skvortsov A,Gunatilaka A.A study of cognitive strategies for an autonomous search[J].Information Fusion,2016,28(C):1-9.
[14]Marjovi A,Nunes J,Sousa P,et al.An olfactory-based robot swarm navigation method[C].2010 IEEE Int Conf on Robotics and Automation(ICRA).Anchorage,2010:4958-4963.
[15]Spears D F,Thayer D R,Zarzhitsky D V.Foundations of swarm robotic chemical plume tracing from a fluid dynamics perspective[J].Int J of Intelligent Computing and Cybernetics,2009,2(4):745-785.
[16]张建化,巩敦卫,张勇.基于微粒群优化的有限通信多机器人气味寻源[J].控制与决策,2013,28(5):726-730.(Zhang J H,Gong D W,Zhang Y.Localizing odor sources using multiple robots based on particle swarm optimization in limited communication environments[J].Control and Decision,2013,28(5):726-730.)
[17]Davidson G P,Goodwin D,Robb T A.Simultaneous source localization and boundary mapping for contaminants[C].IEEE American Control Conf.Montreal,2012:4174-4179.
[18]Gintautas V,Hagberg A,Bettencourt L.Cooperative searching for stochastic targets[J].Ar Xiv Preprint,2011,1103(4888):1-8.
[19]Gintautas V,Hagberg A,Bettencourt L.Leveraging synergy for multiple agent infotaxis[J].Proc of Social Computing,Behavioral Modeling,and Prediction,2008,43(6):32-47.
[20]Masson J B,Bechet M B,Vergassola M.Chasing information to search in random environments[J].J of Physics A:Mathematical and Theoretical,2009,42(43):434009-434014.
[21]张思齐,徐德民.湍流环境中多弱感知机器人气味源搜索算法[J].控制与决策,2015,30(8):1429-1433.(Zhang S Q,Xu D M.Odor source search employing multi-robots with limited perception in turbulence environments[J].Control and Decision,2015,30(8):1429-1430.)
[22]Lu Q,Luo P.A learning particle swarm optimization algorithm for odor source localization[J].Int J of Automation and Computing,2011,8(3):371-380.
[23]Bettencourt L.The rules of information aggregation and emergence of collective intelligent behavior[J].Topics in Cognitive Science,2009,1(4):598-620.
[24]Hajieghrary H,Hsieh M A,Schwartz I B.Multi-agent search for source localization in a turbulent medium[J].Physics Letters A,2016,380(20):1698-1705.
[25]Chen J,Xin B,Peng Z,et al.Optimal contraction theorem for exploration–exploitation tradeoff in search and optimization[J].IEEE Trans on Systems,Man,and Cybernetics,Part A:Systems and Humans,2009,39(3):680-691.
[26]Al-Rifaie M M,Bishop J M,Caines S.Creativity and autonomy in swarm intelligence systems[J].Cognitive Computation,2012,4(3):320-331.
[27]Siggia E D,Vergassola M.Decisions on the fly in cellular sensory systems[J].Proc of the National Academy of Sciences,2013,110(39):3704-3712.
[28]Garnier S,Gautrais J,Theraulaz G.The biological principles of swarm intelligence[J].Swarm Intelligence,2007,1(1):3-31.
[29]Ristic B,Skvortsov A,Walker A.Autonomous search for a diffusive source in an unknown structured environment[J].Entropy,2014,16(2):789-813.
[30]Breugel F V,Dickinson M H.Plume-tracking behavior of flying Drosophila emerges from a set of distinct sensory-motor reflexes[J].Current Biology,2014,24(3):274-286.
[2]Kowadlo G,Russell R A.Robot odor localization:A taxonomy and survey[J].The Int J of Robotics Research,2008,27(8):869-894.
[3]Umbers K L,Symonds M E,Kokko H.The mothematics of female pheromone signaling:Strategies for aging virgins[J].The American Naturalist,2015,185(3):417-432.
[4]Gardiner J M,Atema J.The function of bilateral odor arrival time differences in olfactory orientation of sharks[J].Current Biology,2010,20(13):1187-1191.
[5]Naeem W,Sutton R,Chudley J.Chemical plume tracing and odour source localisation by autonomous vehicles[J].J of Navigation,2007,60(2):173-190.
[6]CardéR T,Willis M A.Navigational strategies used by insects to find distant,wind-borne sources of odor[J].J ofChemical Ecology,2008,34(7):854-866.
[7]Webster D R,Volyanskyy K Y,Weissburg M J.Bioinspired algorithm for autonomous sensor-driven guidance in turbulent chemical plumes[J].Bioinspiration and Biomimetics,2012,7(3):129-135.
[8]Tishby N,Polani D.Information theory of decisions and actions[M].New York:Springer,2011.
[9]Vergassola M,Villermaux E,Shraiman I.‘Infotaxis’as a strategy for searching without gradients[J].Nature,2007,445(7126):406-409.
[10]Rodríguez J D,Gómez-Ullate D,Mejía-Monasterio C.Limits on the performance of infotaxis under inaccurate modelling of the environment[J].Ar Xiv Preprint,2014,1408(1873):1-8.
[11]Null N.Correction:Reactive searching and infotaxis in odor source localization[J].Plos Computational Biology,2014,10(10):e1003861-e1003861.
[12]Moraud E M,Martinez D.Effectiveness and robustness of robot infotaxis for searching in dilute conditions[J].Frontiers in Neurorobotics,2010,4(4):1-8.
[13]Ristic B,Skvortsov A,Gunatilaka A.A study of cognitive strategies for an autonomous search[J].Information Fusion,2016,28(C):1-9.
[14]Marjovi A,Nunes J,Sousa P,et al.An olfactory-based robot swarm navigation method[C].2010 IEEE Int Conf on Robotics and Automation(ICRA).Anchorage,2010:4958-4963.
[15]Spears D F,Thayer D R,Zarzhitsky D V.Foundations of swarm robotic chemical plume tracing from a fluid dynamics perspective[J].Int J of Intelligent Computing and Cybernetics,2009,2(4):745-785.
[16]张建化,巩敦卫,张勇.基于微粒群优化的有限通信多机器人气味寻源[J].控制与决策,2013,28(5):726-730.(Zhang J H,Gong D W,Zhang Y.Localizing odor sources using multiple robots based on particle swarm optimization in limited communication environments[J].Control and Decision,2013,28(5):726-730.)
[17]Davidson G P,Goodwin D,Robb T A.Simultaneous source localization and boundary mapping for contaminants[C].IEEE American Control Conf.Montreal,2012:4174-4179.
[18]Gintautas V,Hagberg A,Bettencourt L.Cooperative searching for stochastic targets[J].Ar Xiv Preprint,2011,1103(4888):1-8.
[19]Gintautas V,Hagberg A,Bettencourt L.Leveraging synergy for multiple agent infotaxis[J].Proc of Social Computing,Behavioral Modeling,and Prediction,2008,43(6):32-47.
[20]Masson J B,Bechet M B,Vergassola M.Chasing information to search in random environments[J].J of Physics A:Mathematical and Theoretical,2009,42(43):434009-434014.
[21]张思齐,徐德民.湍流环境中多弱感知机器人气味源搜索算法[J].控制与决策,2015,30(8):1429-1433.(Zhang S Q,Xu D M.Odor source search employing multi-robots with limited perception in turbulence environments[J].Control and Decision,2015,30(8):1429-1430.)
[22]Lu Q,Luo P.A learning particle swarm optimization algorithm for odor source localization[J].Int J of Automation and Computing,2011,8(3):371-380.
[23]Bettencourt L.The rules of information aggregation and emergence of collective intelligent behavior[J].Topics in Cognitive Science,2009,1(4):598-620.
[24]Hajieghrary H,Hsieh M A,Schwartz I B.Multi-agent search for source localization in a turbulent medium[J].Physics Letters A,2016,380(20):1698-1705.
[25]Chen J,Xin B,Peng Z,et al.Optimal contraction theorem for exploration–exploitation tradeoff in search and optimization[J].IEEE Trans on Systems,Man,and Cybernetics,Part A:Systems and Humans,2009,39(3):680-691.
[26]Al-Rifaie M M,Bishop J M,Caines S.Creativity and autonomy in swarm intelligence systems[J].Cognitive Computation,2012,4(3):320-331.
[27]Siggia E D,Vergassola M.Decisions on the fly in cellular sensory systems[J].Proc of the National Academy of Sciences,2013,110(39):3704-3712.
[28]Garnier S,Gautrais J,Theraulaz G.The biological principles of swarm intelligence[J].Swarm Intelligence,2007,1(1):3-31.
[29]Ristic B,Skvortsov A,Walker A.Autonomous search for a diffusive source in an unknown structured environment[J].Entropy,2014,16(2):789-813.
[30]Breugel F V,Dickinson M H.Plume-tracking behavior of flying Drosophila emerges from a set of distinct sensory-motor reflexes[J].Current Biology,2014,24(3):274-286.