Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 599-602.doi: 10.11896/JsJkx.190500018

• Interdiscipline & Application • Previous Articles     Next Articles

Research on Relocation of Substation Inspection Robot

LI Zhong-fa, YANG Guang, MA Lei and SUN Yong-kui   

  1. School of Electrical Engineering,Southwest Jiaotong University,Chengdu 610031,China
  • Published:2020-07-07
  • About author:LI Zhong-fa, born in 1996, postgradua-te.His main research interests include robot control and so on.
    MA Lei, born in 1972, Ph.D, professor, Ph.D supervisor.His main research interests include control engineering and control theory.
  • Supported by:
    This work was supported by the Joint Fund of the National Natural Science Foundation of China and China Academy of Engineering Physics (U1730105).

Abstract: The environment of substation is complex,and the manual inspection is labour-intensive and inefficiency.The hardware framework of the inspection robot is studied,and the positioning research of the inspection robot is completed based on the Adaptive Monte Carlo Localization(AMCL).This paper provides the corresponding solution in regard to the deficiency of Adaptive Monte Carlo Localization (AMCL) in the practical application of engineering.Considering AMCL can’t restore the location rapidly,a new method of relocation based on database has been put forward,which uses the database to store location values.When the location mismatches,the location value stored in database is used for initializing particles,so as to realize rapid restoration of location.Experiments have proven that compared to the primary AMCL algorithm,the improved AMCL algorithm is more competent in restoration of location after the loss of location.

Key words: AMCL, Relocation, Substation inspection robot

CLC Number: 

  • TP242
[1] ZHANG P C,GAO X.Digital Substation System Structure.Power System Technology,2006,30(24):73-77.
[2] XU H M.Design and implementation of substation inspection system based on Pocket PC and.NET platform .BeiJing:BeiJing University of Posts and Telecommunications,2006.
[3] CHEN Y.Design and implementation of global path planning forsubstation intelligent inspection robot .Jinan:Shandong University,2015.
[4] THRUN S,FOX D,BURGARD W,et al.Robust Monte Carlo Location for mobile robot .Artificial Intelligence,2001,128(1/2):99-141.
[5] KWON T B,SONG J B,JOO S H.Elevation moment of inertia:A new feature for Monte Carlo localization in outdoor environment with elevation map..Journal of Field Robotics,2010,27(27):371-386.
[6] THRUN S,BURGARD W,FOX D.Probabilistic Robotics.MIT Press,2005.
[7] ZHANG L,ZAPATA R,LPINAY P.Self-adaptive MonteCarlo localization for mobile robots using range finders//IEEE/RSJ International Conference on Intelligent Robots & Systems.IEEE Press,2009.
[8] CHEN N.Research on indoor navigation system based on UWB and inertial navigation fusion .Harbin:Harbin Institute of Technology,2018.
[9] JIA Y Y.Research on indoor positioning method of mobile robot based on ROS system.TianJin:TianJin University of Technology,2019.
[10] YANG J Z.Optimization of Positioning and Path Planning Algorithm for Indoor Mobile Robot.Hefei:University of Science and Technology of China,2019.
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