中文摘要：

针对移动机器人探测动态未知环境的问题，引入了一种由贝叶斯理论和Dempster-Shafer证据理论（DST）扩展而来的新的信息融合方法——Dezert-Smarandache理论（DsmT）．采用栅格地图，并根据声纳的物理特性，在DSmT框架下建立了声纳的数学模型．运用DSmT中的高级模型，即混合DSm模型，构造了一组基本信度赋值函数（gbbaf），用以处理动态环境下声纳获取的不确定和不精确信息，甚至于高冲突信息．借助Pioneer 2-Dxe移动机器人分别进行了混合DSm模型和DST两种算法的地图构建实验，并绘制了相应的二维基本信度赋值地图．将由混合DSm模型与DST构建出的环境地图进行了比较，充分验证了混合DSm模型在未知动态环境下的有效性，为处理动态高冲突信息提供了有力的理论依据．

英文摘要：

A new information fusion method, named Dezert-Smarandache theory （DSmT）, which is extended from Bayesian theory and Dempster-Shafer theory （DST）, is introduced to solve the problem of mobile robot map building in an unknown dynamic environment. The grid map method is adopted, and according to the characteristics of sonar sensors, a sonar sensor mathematical model is constructed based on DSmT. With the application of hybrid DSm model, i.e., an evolving model of DSmT, a group of general basic belief assignment functions （gbbaf） are constructed to deal with the uncertain, imprecise and even highly conflicting information obtained with sonar sensors in the dynamic environment. At last, mobile robot Pioneer 2-Dxe is used to carry out experiments of map building with hybrid DSm model and DST, and the correlative 2D general basic belief assignment （gbba） map is constructed. The ichnography created with the hybrid DSm model is compared with the map built with DST, and the result verifies the validity of the hybrid DSm model in dynamic unknown environment, supplying a powerful theoretic evidence to process highly conflicting dynamic information.