可移动机器人在中心对称环境中的自定位算法

可移动机器人的自定位问题是智能机器人研究中的重要课题，它包含许多传感器技术和定位算法，马尔可夫定位算法的优点是可以使机器人在全局不确定的情况下估计它的位置。这种方法采用概率分布描述机器人的位置信度，机器人通过在运动过程中所获得的传感器数据和运动记录来更新信度分布，然后采用最高信度值来估计它所在的位置。对于只有距离测量传感器的机器人在中心对称环境中仅仅采用马尔可夫自定位法还是无法确定其位置，为了解决中心对称的环境中所存在的问题，建议在机器人上装上陀螺仪或指南针，定义一个角度高斯分布函数，并利用这个函数建立新的机器人感知模型来扩展马尔可夫定位算法，通过仿真程序对多种对称情况进行实验，验证了这一新算法的可行性，这个扩展马尔可夫自定位算法不仅可使机器人在中心对称环境中很快地确定自己的位置，而且可以加快非对称环境中信度分布收敛到真实位置的速度。

Mobile Robot Localisation in a Symmetrical Environment

Mobile robot localization is an important topic in the intelligent robot domain. It includes sensory techniques and localization algorithms. An advantage of Markov localization is that the localization algorithm provides a means of estimating robot position under global uncertainty. In this approach a probability density distribution is employed to represent belief of robot position. The belief distribution is updated by receiving the sensory data and reading the record of robot moving. The maximum of the belief is applied to estimate robot position. However, in a symmetrical environment a robot using distance detector cannot find its position by means of Markov localisation alone. In order to solve this problem, a robot equipped with a gyroscope or a compass is proposed, and the Markov localization algorithm is extended with a new perceptual model that is constructed using an angle Gaussian distribution defined in this paper. Experiments for simulating different symmetrical environments have been done. The experimental results show that the new algorithm is feasible and that a robot with the new algorithm can quickly estimate its position when moving in a symmetrical environment. In addition, the algorithm converges at the real position in an asymmetrical environment faster than regular Markov localization.