A FUSION MACHINE BASED ON DSMT AND PCR5 FOR ROBOT'S MAP RECONSTRUCTION

作     者：XINHAN HUANG XINDE LI MIN WANG JEAN DEZERT 

作者机构：Intelligent Control and Robotics Laboratory Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China ONERA 29 Av. de la Division Leclerc 92320 Chǎtillon France 

出 版 物：《International Journal of Information Acquisition》 

年 卷 期：2006年第3卷第3期

页      面：201-211页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Uncertainty DSmT grid map information fusion mobile robot PCR 

摘      要：Characteristics of uncertainty, imprecision, and even imperfection are presented from knowledge acquisition in map reconstruction using sonar sensors fixed on autonomous mobile robot. In order to improve the precision of the fusion and performances of map reconstruction, we propose in this paper a new fusion machine based on Dezert-Smarandache Theory (DSmT) coupled with the fifth Proportional Conflict Redistribution rule (PCR5) for dealing with uncertain and conflicting evidences provided by homogeneous or heterogeneous sources of information. We propose a belief model for sonar grid map and show how to construct efficiently generalized basic belief assignment functions for sensors onboard. A Pioneer II mobile robot with 16 sonar range finders serves as the experiment platform. In our experiment, the robot evolves in a real environment with some obstacles and the environment map is rebuilt online with our self-developing software platform. In this study, we also compare our new approach with other ones based on probability theory, fuzzy theory, Dempster-Shafer Theory (DST) and gray system theory. Our results show an improvement of the performances in precision of map reconstruction of mobile robot with respect to those obtained from aforementioned classical approaches.