期刊文献+

基于学习进化的机器人同时定位与地图创建

Mobile Robot Simultaneous Localization and Mapping Algorithm Based on Learning and Evolution
下载PDF
导出
摘要 利用学习与进化结合的思想,改善基于粒子滤波的SLAM算法。在对学习与进化的关系深入分析的基础上,针对基于粒子滤波的SLAM算法,提出将滤波过程分成学习和进化两个阶段,分别给出相应算法解决粒子有效性与多样性的问题,缓解二者之间的矛盾,改善了SLAM算法的效果,增强了算法的鲁棒性,也验证了学习与进化的关系。最后,通过多次Monte-Carlo仿真实验结果表明了该算法的有效性。 The main contribution is utilizing both learning and evolution method to improve the performance of SLAM algorithm based on particle filter.The particle filter was considered two parts:first part played the learning role and the another one played the evolution.These two parts could be used to solve the sample impoverishment problem and the degeneracy problem for particle filter respectively.In such case,the filter was more robust and performs better.For this purpose,different algorithms for each part were proposed.In the mean time,the relationship between learning and evolution was proved again.Finally,the result of Monte-Carlo simulation proves the algorithm is valid.
出处 《系统仿真学报》 CAS CSCD 北大核心 2010年第5期1204-1209,共6页 Journal of System Simulation
基金 国家863项目资助计划(2006AA040203) 国家863项目资助计划(2009AA04Z213) 国家自然科学基金(60875057)
关键词 学习 进化 粒子滤波器 同时定位与地图创建 learning evolution particle filter simultaneous localization and mapping (SLAM)
  • 引文网络
  • 相关文献

参考文献16

  • 1Hugh Durrant-Whyte, Tim Bailey. Simultaneous Localisation and Mapping (SLAM): Part I - The Essential Algorithms [J]. IEEE Robotics and Automation Magazine (S1070-9932), 2006, 13(2): 99-110.
  • 2Tim Bailey, Hugh Durrant-Whyte. Simultaneous Localisation and Mapping (SLAM): Part II - State of the Art [J]. IEEE Robotics and Automation Magazine (S 1070-9932), 2006, 13(3): 108-117.
  • 3M Montemerlo, S Thrun, D Koller, B Wcgbrcit. Fast-SLAM: A factored solution to the simultaneous localization and mapping problem [C]//Proc. AAAI Nat. Conf. Artif. IntcU., 2002. USA: AAAI,2002: 593-598.
  • 4M Montemerlo, S Thrun. Simultaneous localization and mapping with unknown data association using FastSLAM [C]//Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), Taipei, Taiwan, China, 2003. USA: IEEE, 2003:1985-1991.
  • 5M Sanjeev Arulampalam, Simon Maskell, Neil Gordon, Tim Clapp. A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking [J]. IEEE Transactions on Signal Processing (S1053-587X), 2002, 50(2): 174-188.
  • 6M Montemerlo, S Thrun, D Koller, B Wegbreit. Fast-SLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges [C]// Proc. Int. Joint Conf. Artif. Intell., 2003. Acapulco,Mexico: IJCAI, 2003:1151-1156.
  • 7M Montemcrlo, S Thrun. FastSLAM: A Scalable Method for the Simultaneous Localization and Mapping Problem in Robotics [M]. Berlin, Heidelberg, Germany: Springer-Verlag. 2007.
  • 8N M Kwok, A B Pad. A Modified Particle Filter for Simultaneous Localization and Mapping [J]. Journal of Intelligent and Robotic Systems (S 1573-0409), 2006, 46(4): 365-382.
  • 9J F Dong, W S Wijesoma, A P Shacklock. An Efficient Rao-Blackwellized Genetic Algorithmic Filter for SLAM [C]//IEEE International Conference on Robotics and Automation, Roma, Italy, 10-14 April 2007. USA: IEEE, 2007: 2427-2432.
  • 10张家奇,陈启军.学习对进化的影响研究及仿真验证[J].系统仿真学报,2007,19(24):5849-5851. 被引量:9

二级参考文献15

  • 1Baldwin J M. A New Factor in Evolution [J]. American Naturalist (S0003-0147), 1896, 30(3): 441-451.
  • 2Bull L. On the Baldwin Effect [J]. Artificial Life (S1064-5462), 1999, 5(3): 241-246.
  • 3Waddington C H. Canalization of Development and the Inheritance of Acquired Characters [J]. Nature (S0028-0836), 1942, 150(6): 563-565.
  • 4Hinton G, Nowlan S. How Learning Can Guide Evolution [J]. Complex Systems (S0944-2006), 1987, 1(3): 495-502.
  • 5Kolen, J F, Pollack J B. Back-propagation is Sensitive to the Initial Conditions [J]. Complex Systems (S0944-2006), 1990, 4(3): 269-280.
  • 6Miller G F, Todd P M, Hedge S U. Designing Neural Networks Using Genetic Algorithms [C]// Proceedings of the Third International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufrnann, 1989: 379-384.
  • 7X Yao. A Rreview of Evolutionary Artificial Neural Networks [J]. International Journal of Intelligent Systems (S0884-8173), 1993, 4(2): 203-222.
  • 8Liu Y, Yao X, Higuchi T. Evolutionary Ensembles with Negative Correlation Learning [J]. IEEE Transactions on Evolutionary Computation (S1089-778X), 2000, 4(4): 380-387.
  • 9M Rocha, P Cortez, J Neves. The Relationship between Learning and Evolution in Static and Dynamic Environments [C]// Proceedings of the Second International Symposium on Engineering of Intelligent Systems. 2000: 377-383.
  • 10M Rocha, P Cortez, J Neves. Evolutionary Design of Neural Networks for Classification and Regression [C]//ICANNGA Proceedings. Coimbra, Portugal: Springer, March 2005: 288-291.

共引文献8

;
使用帮助 返回顶部