• DocumentCode
    402878
  • Title

    An evidential approach to environment sensing for autonomous robot

  • Author

    Wang, Xue-song ; Peng, Guang-zheng ; Ji-Fei Hao

  • Author_Institution
    Dept. of Autom. Control, Beijing Inst. of Technol., China
  • Volume
    1
  • fYear
    2003
  • fDate
    2-5 Nov. 2003
  • Firstpage
    210
  • Abstract
    In order to improve the correctness of the environment sensing, multiple ultrasonic sensors are applied to acquire the information of robot´s surrounding environment and a modified evidential theory is applied to analyze and fuse sensor data. Sensor model and definite discrete beliefs to ranging data are presented according to the physical characteristics of ultrasonic sensors. Some issues such as the data fusion sequence, the data fusion level, the definition of the frame of discernment, the choices of evidences, the modified fusion algorithm and the establishment of decision rules are studied profoundly which all belong to the domain of the application of evidential theory to environment sensing. Computer simulation results indicate that although approximation and neglect during algorithm inference and simulations will lead recognition error, the error has little influence on robot navigation. So the proposed method has very perfect effects on environment sensing.
  • Keywords
    case-based reasoning; distance measurement; mobile robots; navigation; path planning; sensor fusion; ultrasonic measurement; ultrasonic transducers; uncertainty handling; autonomous robot; data fusion level; data fusion sequence; definite discrete beliefs; discernment frame; environment sensing; evidential theory; fuse sensor data; multiple ultrasonic sensors; recognition error; robot navigation; Application software; Approximation algorithms; Computer errors; Computer simulation; Fuses; Inference algorithms; Information analysis; Robot sensing systems; Sensor fusion; Sensor phenomena and characterization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2003 International Conference on
  • Print_ISBN
    0-7803-8131-9
  • Type

    conf

  • DOI
    10.1109/ICMLC.2003.1264472
  • Filename
    1264472