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    基于混合DSm模型的多机器人地图构建

    Multi-Robot Map Building Based on Hybrid DSm Model

    • 摘要: 针对多移动机器人探测静态未知环境,提出了多机器人的一类新的体系结构和机器人内部控制结构,采用分布式融合框架,引入了最近提出的一种在贝叶斯和DST扩展而来的信息融合理论DSmT,结合限制传播算法,建立了不精确传感器(声纳)的混合DSm模型,构造了基本信度赋值函数,计算每个栅格的基本信度值(gbba),有效地融合了多个移动机器人使用声纳获取到的不精确、不确定和高冲突环境信息.最后,以Pioneer 2-DXe机器人作为实验平台,将由混合DSm模型构建出的静态环境地图与实际环境布局做比较,并利用openGL绘制出三维置信度分布图,充分验证了所提出的算法和基于通信的多机器人系统的有效性.

       

      Abstract: A new multi-robot system framework and a novel kind of robot control structure are introduced to solve the problem of multi-robot map building under entirely unknown environment Firstly. In order to deal with a mass of data, distributed fusion framework is adopted in this multi-robot system. Compared with the classical architectures, this multi-robot system enhances overall system performance, especially real-time performance. Then a few general basic belief assignment functions (gbbaf) and a hybrid DSm model of sonar are constructed to deal with the uncertain and imprecise, sometimes even high conflicting information, which is obtained by sonar sensors with the application of new information fusion method Dezert-Smarandache theory (DSmT). DSmT is extended from Bayesian theory and Demster-Shafer theory (DST) in the system and consideration of characteristics of sonar sensors. Finally, Pioneer II mobile robots are used to carry out experiments of map building for both single-robot system and multi-robot system. And a 3D general basic belief assignment map is structured by openGL. The comparison of created ichnography with the real map testifies the validity of hybrid DSm model and efficiency of multi-robot system for fusing imprecise information and map building proposed in this research.

       

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