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
Link To Document :
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